Friday, September 30, 2016
Wait-free Bounded vs Wait-free Unbounded
Within the progress condition of wait-free there are two major groups:
- methods/algorithms that provide wait-free bounded guarantees;
- methods/algorithms that provide wait-free unbounded guarantees;
The difference between the two is subtle but very important.
Wait-free bounded means that there is a known bound on the number of steps, which itself has a strong but hidden implication: that there is a bound on the number of objects to be tracked, and therefore, to be deleted.
Wait-free unbounded means there is no known bound, and therefore, there may or may not exist a bound on the number of objects to be deleted.
This is confusing but let's consider a concrete example:
This year at PPoPP there was a wait-free queue presented by Chaoran Yang and John Mellor-Crummey. http://dl.acm.org/citation.cfm?id=2851168
John Mellor-Crummey is the MC in the MCS lock, so you may have "heard of him" before ;)
This queue uses fetch-and-add (FAA) and is wait-free unbounded. In the meantime, we designed a few algorithms based on fetch-and-add that are also wait-free unbounded (yes, yes, we did some wait-free queues).
The problem with this queue (and the wait-free unbounded queues we did as well) is that the dequeue() may have to traverse an unbounded number of nodes to find a node to dequeue. The basic idea is that you read the current head and then do a FAA on a certain monotonic counter to take a ticket and then traverse from head until you find a node that matches your ticket. All nodes have a monotonically increasing and unique ticket.
The problem with this approach is that between reading the head and doing the FAA the thread may go to sleep, and by the time it wakes up, there could be a very large number of nodes that were dequeued (and enqueued) and the counter in the FAA is for a ticket that is very far from the head.
This is bad enough on itself for latency at the tail of the distribution because now we have to traverse all these nodes which can take a long time, but it gets worse.
The fact that we have to traverse these nodes to find the one containing our ticket (with the item we need to return) means that all those nodes need to be live. In other words, we can't delete any of those nodes.
Do you see the problem with that?
No?
That's ok, Chaoran and John didn't seem to see it either and they know a lot about concurrency.
If you can't delete those nodes even though they have been dequeued, how much memory do you need to keep them alive?
1Mb ? 1Gb? 1Tb? Even more?
Notice that the number of nodes that needs to be traversed is finite, but we don't known how large it is because it's unbounded.
So, how much memory do we need to store an unknown amount of nodes?
See the problem now?
Quoting from Harald Hefgott which is one of the top mathematicians of present times: "Computers today are very fast and can also perform calculations in parallel. But the memory is still limited,"
Maybe you think this is purely theoretical.
In that case, consider what happens if we have oversubscription of threads to cores. Let's say we have 8 cores on our machine and we fire up 80 threads to dequeue from the queue, and maybe another 80 threads to enqueue just to keep it balanced. Only 8 threads at a time will be scheduled, and depending on the OS, each thread may be scheduled for few hundred milliseconds (sometimes called the "quantum").
It will take 20 "cycles of scheduling" until a thread is scheduled again, and during that time a lot of dequeues can occur.
If there is a thread calling dequeue() and reading the head and goes to sleep just before doing the FAA, then from that moment on, no further nodes in the queue can be deleted.
Let me repeat this last part because it's important: No further nodes can be deleted, not newly inserted nodes, nor already existing nodes, none, zero, nada, zilch, niente.
The 20 cycles take about 100ms x 20 = 2 seconds, and on average half the time we dequeue (the other half we enqueue) so, how many nodes can we dequeue in one second?
Modern computers can do a few million such operations per second and perhaps, per core, depending on contention, but let's say the answer is Z.
Each node in a queue takes at least 3x64 bits, with 64bits for the next pointer, 64bits for the value pointer, and 64bits for ticket.
We need to keep in memory at least 24 x Z bytes. If Z is a really large number then this may be more memory than there is available on the machine, and Z can increase with more oversubscription, or higher quantums, or and unfair thread scheduler, making the problem worse.
In practice this is a dangerous approach.
In theory, the fact that the program can crash due to running out of memory, kind of implies that these algorithms are not wait-free, which is a bit of a contradiction.
Wait-freedom implies resilience to faults, i.e. a thread is suppose to make progress and complete its work in a finite number of steps, regardless of what the other threads are doing (even sleeping).
If having a single sleeping thread can crash any of the other threads and even the entire application due to memory exhaustion (an extreme case, but quite possible), is this really wait-free?
I don't think so.
In other words, we have one thread calling dequeue() and it goes to sleep for a while, and this simple fact causes any of the other threads to fail, whether enqueues or dequeues or even threads doing something else in the application which will run out of memory and stop working correctly.
What kind of wait-free queue doesn't give a decent latency guarantee at the tail, and it can cause program crashes for no particular reason? These wait-free unbounded queues are not really wait-free.
What would you think if I came up to you and said:
Hey! I invented this really cool wait-free (unbounded) queue. It's great because it's wait-free... it has one small quirk though, it can sometimes cause your application to crash!
Whether you think it's wait-free or not, I doubt you would be willing to deploy such a queue in production if you heard me say it like that ;)
Are all wait-free unbounded queues and data structures affected by this issue?
I don't think so. If the "unbounded" part of the wait-free is due to some calculation that has an unbounded number of steps and not due to traversing an "unbounded" number of nodes, then it should be safe.
Unfortunately, I don't know of any other wait-free unbounded data structure to provide an example. If you know one, please use the comments section!
Thursday, September 22, 2016
URCU ReadersVersion in C++
On the previous post we showed a new Userspace RCU (URCU) algorithm that can be implemented with just a atomics and the Java/C11/C++1x memory model.
Here is the implementation in C++:
https://github.com/pramalhe/ConcurrencyFreaks/blob/master/CPP/papers/gracesharingurcu/URCUGraceVersion.hpp
Not only is this code simple enough to just copy/paste into your codebase, but it has a pretty good throughput and scales well.
This seems to be the best basic implementation of an URCU (with just atomics) in all possible scenarios. There may be other implementations that surpass it, but they would definitely have to use some non-portable trickery, which URCUs usually do, stuff like using POSIX signals, or some CPU special instructions, or some Kernel API.
Anyways, if you're interested in portable low-overhead memory reclamation for C/C++ that is wait-free population oblivious for readers and blocking for updaters, yet capable of aggregating grace periods, then this should do the trick for you!
We did a simple benchmark against two of the URCU implementations in the userspace urcu lib http://liburcu.org/ and we'll make the benchmark available in a couple weeks, but in the meantime here are some plots below.
We compared against the Bullet-Proof URCU and the default URCU. More details in this readme https://github.com/urcu/userspace-rcu
The first plot shows just readers, i.e. calls to rcu_read_lock()/rcu_read_unlock(), where the read-side critical section is just an atomic load on a global variable. This means the read-side critical section is as short as possible, and that if there is any contention is caused by the overhead in rcu_read_lock()/rcu_read_unlock(). As you can see from the plots, all three scale well, but the ReadersVersion is the one that scales better.
![](data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAeEAAAGnCAIAAAAPKXgoAAAgAElEQVR4nO3d+1dTZ/4v8Pnz+p2eMdKKGu2MTKcez9IzZ1yrXa6scU6nUYGBkfrFhmXBu3MYh36tNHxlLJdNEhAQNwrILQpyC+FuYiE0TdOsnB+eZGffsnPbez9PkvdrZa3akOw8gfD28bl8nl/dvHmzBgAA2HPz5s1f1dTUxAEAgD01NTXIaAAARiGjAQDYhYwGAGAXMhoAgF3IaAAAdiGjAQDYhYwGAGAXMhoAgF3IaAAAdiGjAQDYhYwGAGAXMhoAgF3IaAAAdiGjAQDYhYwGSPA7bVarg4/H4/E477BabU6/9mOTDy4uubxNoA8ZDebyO23ZxwLv0C0IM19K+gjtx+f0LnRom46XyuVtAgOQ0WAuZjNa/gBxd1NO34hm9m0CC5DRYC5WM1r59fTP0DmiWX2bwARkNJiL0YxW+3K6p+gd0Yy+TWADMpppyX+I8g6r1WoVBwOZs7LK7lZJj1x+A9O/XPIeq1XlapIvKrNL9GUHX3hGS15N5Z2pf2fklyJX0frGpc1i2d0Zv+fCA6TfCNbfJjACGc008utjs6llgOT3MvE/emS04uWkSxhkCxp4hzISxK8maaqQO/lmtOyNk7aIL5b2OyO5VCLf5BdWfJdkV1O/M7uMFr9rxZoQFt8msAIZzTTlb1lc81dKj4xWezllZqf7lVZZNSB5qKxjl0Gmq8keoRU2qQcq/yLJsses0iT1B6lltPK7IPurja23CcxARjNN7bdH6zdKl350hkzSvqQkP3JtvvrVJD14xTNFD8nmLw+V5JK3WtbWTDNsWWa01nPYe5vADmQ007IMTa3HF5jRkmFP7RHS1Mup/cNb6y2lpew+av1DXfOtph0MVrRa1lbl0I30+XpnNAtvExiCjGYaKxmt+XxljEsyWrd+tA7h5eBVBh7SNDSu8uaz/EuHdkYX+jaBJchopqXNaKpjHfJniJ9i4FiH+hsXPURz7kt4nNqge5qUUlvBoXiUIRlN820CW5DRTFP97cluykh0gYIyWruTpUhwaeOYnDNMu/wkw4KHdFGW6XueY0bTfpvAGGQ009RjQd559Ttt6r+/wuBkARmt0lcWvZ7aurz0XU/eQZb25bv2Tu2Nq8Sj6ndGJQe1FzmrvjnVhmf4nuea0XTfJrAGGc20DLkgH/9NPSd1f3Jfio4vp7LSIfUF9aV6onYqHqAh3RSdektUmpo2B+U5l7HzqjkgoPk9zzmjab5NYA4yGiAeV0uq7CO6iGR4m8AeZDRAPB5XZlVJRnSGtwkMQkYDECpDudnM0hUbjbcJLEJGlw/pmLKC6RHEWnskw8i5jJoXmTJ5myUDGQ0AwC5kNAAAu5DRAADsQkYDALALGQ0AwC5kNAAAu5DRAADsQkYDALALGQ0AwC5kNAAAu5DRAADsQkYDALALGQ0AwC5aGS3UPEsU3UodOyE/dwmVEwGgfFHJaPH5e7I7eAdJbeU9AADlh0ZG+502aeqK7yB/Vt5jagsBANhAI6Mltd0dPDIaACANShktOoNINZGR0QAAceoZnS6R88jo2dlZg9oLAEALpfHoREgn/6THnKHL5TKwzQAANNBZe6dcVydfi6d2jzZkNACUntLZw4KMBoDSg4wuaqKhfcn/pv6hovkvk3RPJ1QvUkjzsnqC+JUUC+nzuQhAUUNGUxOJxhoeTkWisQKuoZHRyZhKJJYo7vxOW+JPGTNadpECm5fdM1IvlF9EA5QUZDQ17YPLFRf72geXC7hGFhlN7lQP2awzWrYWR9K7Fi93l97hcKR9lt9pczidNpVLqHfzs326dLm96FkOPh6P8w6b0+kQ3wPAOmS0bu5x8xY7J9wanTMa90eiscpql8Xem+XjxfcL92Tfj+bV1y/m0Y8WP0Z1w6jNJqnBIizOkT6LdwoPk72s2kvl8HRRs8R/L5E/8w5xgRgMiEAxQEbr5tzNUXG2nmwc1Li/fXD54EXOYucsFzjLF5kfL75fuCer8WgHH0+bSFmPR4sT0irh4OXdYNE1khdUPsspbhB5vvzvg1Rrcnq68GYV0e/gxXflPg4DQAMymoJINHa83iOk8PF6T76j0rIhDGU/Ojmkq55I6Z5OKC4SVwt78aAxCVPVjFY+S/6XhvBkoc8r3emU7dOR0VBSkNEUtA8uH651Cxl9uNad96g075BWc1VGaSKHxQ/kHeJl6SpPj6e5SFwR65JHJfu74u5vmo1Imh173iEZys716SpjHYn3hoyG4oOMpuDU1UHxaIbFzp26Opj5aerkU3jJOxV76VMPlC9vU7k73UWkzxD6rcn/czjIWHHierywgkT+LK3rJUYvFNVrs3166oupQRhyBzIaig8yGgCAXchoAAB2IaMBANiFjAYAYBcyGgCAXchoAAB2IaMBANiFjAYAYBcyGgCAXchoAAB2IaMBANiFjAYAYBcyGgCAXchoAAB2IaMBANiFjAYAYBcyGgCAXchoAAB2IaMBANiFjIYUchJg8qxWjRP/VM4g1LhgPO0hsapSpxAWdOJg4jI4tBCKGzIaUqRJqp3RyiPDtR6XJqNV7xZeV3FSeS5wpiywIBKNNTycjkRjeV8BGU3B8Mk/9L3/nvg2fPIPeV1JcdC132lzOJ22xEHYohO7efXHSy9HIjMZnLzDSq6lPDRcmtHpLy7PaMkp5HzqfyQNSV0n9SzRm0p8QfZU6T2prjhyGqhqH1yuuNjXPric9xUKz2jJP3ulTP39KKKM7nv/PffB34hvfe+/l9eV1DJaSDKVbmiuGW1N01tWHevImNFO0QOkL6XeyOQbEL8peWPIn5X3oB8N9EWiseP1Hou993i9J++utJ79aMlvBe/IevxRH3QzemeUH/r4hKx3rHFTZnSWt6GPT6z1dCdfVi2jRRGt+BHIHy90k4VQlPWjebVnSq8t5GOmjHY4bPK/w9NltNDX9qu8E2lTeIfV4VDcwyOjgb72weUjtW6LnTtS6867K61jRstS2e+0lVE/OqeALiSj+95/z11xIPmyhWZ0xrGOLDJa+FIWGS1vkHY/Os2jkNHAtkg09mBgyVrnOVzjstg5csu7K61jRkv/aa05lWQEuhm99E2baRn9qumr5Mum0svvtMk7pipjHYrHx1W+rDLWIb+Uaj9a5eKKsQ7ZZyKvjBZ/thINU96DjAY6ns5unWwctNg5yxfch5f6hIzOuyut75yhaNWUyQlNO6Nzot94tOhb7nA6bfLBA5WpM9njpReTZ7TN6XTIL5B8oNpPWnFxjTlDIcQ15gwlzVJ91+IBc+k9yGgw3333AknkM03DVQ0DQkCT26mrQ3lcE+s6KPB8WCHrGns+rKDdKAAoVMfwyrE6T8fwio7XREYDAOQpEo3ddy8cq/Pc4+YNegldM1o81IG1dwBQ0rixNWFA48HAkkGvouu6DtPHoMWQ0QBgmnvcPEnnczdHvb53xr2QcWvvzIaMBgDTcGNr5+++eDq7ZfQL6TnWkUvdHP0howHAOIG9yLVOr8XONTpnzHxdfcc6sBccAEoN2ZNyrM5DBjce86tmvjrWdQAAyInr1QlDz/bWcd/2vsktoZLRoh63eHeYtPOtvEcbMhoA9ELq1bV5FuPx+NPZLXvr+PO5HSotMW6foUayKmYXU1uNk4tDlPdkgowGAF1sBsMfXuqz2Hs/uNRXSOlnXVCp1yHPaFmRCVnNyiynIpHRAFA4bmytstplucBZ7NyhalchpZ91QaXunby2AjIaAFjQMbxisXMWe2/h9er0QrfuXeJByGgAYMHzuZ1TV4cqq12F16vTC5XxaMkTHDwyGgComVwMnGkaPn/3BfnfU1eHdKlXpxeqa++EM5AwZwgApgvsRRqdM8KWbtrNUafzWVla9eWTxDWElWWNxaXj5eWJNSGjASB7vu19si2lstplXEWkwum7z1CcymbXWEdGA0D2vL53ldUue+v4ZjBMuy1acJ4hAJSLSDR2j5u//O0U9VXP2TNyrKOczjMEAMY9n9shJw1WVrtC4Sjt5mRL3zlD1aFmkyCjASCdy99OCScNGlruWXeoqQQpZJlj8t9DWc4opH9YYto327+t6da2hRIWCkfZnxtMR/+M9vM8ld8zZHThFKeKF5TRuc4aJ18dWQ36WFjfve9eIMMavu19xucG09F5PDq1WM70X7Siy+hfYj9zLxt/if1cwDXEMcg7rA7e77Q5nE5b6ocgHXlSPF56OekGIt5hJddSWwEpLI10OBLXEQ10kc646KXFm5vIlVRa4nfabE4+dRWzR8ug6AkFRcncIBnc6J/coN2ugui/riO5vAPrOjIYX3S2dB8ZX3QWcA21jJbUe5X9CHLNaOFasp1Eqf/1O21Wq4OXXC1xAfV+tMoXxRmNfjTkjxQUvfbIKxwFe63TW0RLOFTpW6+D/PKVYz96ZXustf/0112HcrlVXu85+nVXZU7Pau0/7V0V3qlaRosiWvETkD9e6CaTe5X9aF7tmSr/I54tTl5Q/ihJTxoZDTqLRGPH6z0We2/FRTOOgjWNcfU6zP41o5vRuQf0oZbuw9d7rC3dh3N94vUea/JlC83ojGMdOWS0xouJe/ToR4Mxrj3yHql1k7nBv//XJO3m6AbrOvTxYuFhrp3olm7r9R5rS7c11650/0xL8mVTcZYYc5AVo5KnsOLxcZUvq4x1yC4l+l/RWIfKkHXqQeIBGGmtQ6ElyGjIj297/7Mbo0wVFNURMpqC8UXnLe6313us5HaL+23+o9KpuTun0ybvz0on7tQeL72YPKNtTqdDfgHhkcl/LvGJ60iGOxITheIh6tQcozBrKG2J0PbEgzFnCFl4MLBUWe2yfMEdvMCxU1BUR/qORwujmhQGO4ooo1v7zyhGmc/QbhRA8bnR9ZqE8tG/eZgqKKojA+p1JLpPqKkEAMbixtbsreOTiwHaDTGQ/nXvsPYOAIzzdHarqmGg0TlDuyEm0XM8WjKMWGZr7wDAaIG9iL11nAxlXP52inZzTII5QwAoAl7fO1KS/1id5zG/Srs55kFGA0AReD63Q7rPgb0I7baYChkNAIwiZTeudXppN4QmZDQAsIgc1110Jfl1Z9x5hmZDRgOUjGudXjI3eLb5WWmU3cibzrVJKYY0MhqgNGwGw8Vbkl93+vajZbA+GgCyFdiLcGNrpM7GwvpukZbk1x3GowGAvkTZDTv3fG6HdlvYgrOyAICmhfXdczdHyehzo3OmZOrV6QVnZQEATWTxxsnGQfSgVeGsLACgQBhu7p/c6BheQfc5HZyVBQCmEspu3Hcv0G5LEcBZWQBgHmFu8FidB4Mb2cC6DgAwibAzpabtZbmV3cgbMhoATPJgYOls8zN0n3Oia0YrTrQzEzIagEFe3zt76ziGnvOm75yhKJhVzok2FjIagCmhcLTROSMsfKbdnGJlwHmGCVh7B1C+FtZ3qxoGhLIbWFqXN53PypL0o80d7UBGA7DjMb9qsXPn777wbe/TbktxM7SmkqkD08hoAOoe86vCQVYoiqQLrOsAgHxEorGGh9PCIIbX9+5s8zMyuEG1XaUGGQ0A+WgfXK642Nc+uByPx290vSZzg2eahicXA7SbVlKQ0QCQs0g0drzeY7H3Hq/3vFp9RwL6HjePuUHd6ToeLRl5xroOgJLVPrh8pNZtsXNHat3tg8vc2BpGnw2ic23S5Oo73mH6LhZkNIA5ItHY4VoX6Ttb7Nzxeg+6z8bRd6zD77RZHU6nzfyKSshoAFP4tvdPO4YtFzgho0lXmna7Spbu49Gm1yRNQkYDmKCqYeCgPRXQ5Hbq6hDtdpWswjNaUqQDZ84ClLb77oVrnd5QOEq7IeWC3roOv2RIJJX0yWBX3qMNGQ1ghEg0do+bt9i5/skN2m0pRwbNGcoKLKk+2OZ0Jit8pB6dvIbynkyQ0QC6ez63Q84bxIndtOi7F1ycypoFO8gXRUcfisPd5vQr78n48shoAH09GFgi6Xy2+ZnX9452c8oUlbp3yS8howEYdqPrNalaR7shZc3IsY40yZrKXGQ0AGMCe5EbXa+FukhAnf7rozMt6lCsA3HwyGgAFnQMrxyr85CaorTbAglU63XwmDMEYEJgL3Lu5igZfba3juNAWHawkdGi4tMa92hDRgPkjRtbs9i5qoYBLLBjjb4ZLS7zjzNnAVjn295fWN8lf55cDGBnCoNw5ixAOQqFo2RnisXOIZpZhjNnAcrO87kdciCsxc7ddy/Qbg5oQT8aoLwE9iKV1S6LnTt3cxQ7U9iH8WiA8hKJxrACuojgrCyA0hfYi9hbxyurXeg4Fx1kNECJezCwRAY3jtV5fNv7tJsDuUFGA5Syy99OkbnBy99OYWdKMUKNf4BSZm8dP9k4iLKixQv9aIBS4/W9s7eOI5dLAzIaoHSEwtFG5wwZ3LjHzdNuDuhA14wWL73DWAeAuby+d2RnSmW16757IRKN0W4R6EDfc1jonAhOIKOhzJHpwfN3X2DxRikxbi+42ZDRUJ6ez+2QVc+hcBTLn0uPnmMdWRbjNwgyGsqNb3ufFH0+2/yMdlvAKPqOdWDtHYBJ7rsXyM6UqoaBycUA7eaAUbCuA6AIRKKxhofTwjRg/+QGWbxxrdOLyqKlDRkNUATaB5crLva1Dy6T/w3sRe67FzD6XA4w1gHAukg0drzeY7H3HrzAWes8WFRXVozqR5s/f4iMhlL1z76FDy72Weyc5QJ36uoQMrqsGDfWgXNYAHQQ2IscvMCR0WeLnTtej350eTEso6WnspgAGQ0lqc2zWEE60XbOYueO1LqFUWkoBwaOR5t8EAsyGkpGJBq7x82fv/tiMxg+dXVICGhyO3V1iHYDwTxY1wHAlsnFwJmmYRLHWLkBBWc077BSLdMhQEZDCbjHzZN0PtM0jICGuE796OQoB+p1ABTmbPOzymrXg4El2g0BVug61iEcyWL2meDxODIaihbZkEKK1YXCUSzbADFDxqOpZDUyGopRx/DKsTqPxc7d6HpNuy3AIkPnDP1Om3kDIMhoKC6bwTCpWmexc/bWcZTdAFW6rr2TdJuxhwVAy7VOL6lah4MHQYOe/WjRQSy8w/RRaWQ0FIWF9d3AXiQej/u29zuGV9B9Bm36jnX4nTarw2nmCEcKMhoYF9iLkANhUZIfsqf7eDS1w1iQ0cAybmyNzA1WVru4sTXazYGiUXhGC4s4lDAeDRCPx+PP53aEucHNYJh2c6CYYC84gOF82/v21vH+yQ3aDYHiY9Ccofll75DRwBav792ZpuGqhgHsSYFC6Fv3TpzKsv81HDIaGEGq1pHBjZONg8hoKISuGS2ZLMT6aChHoXBUqFp3j5vH0jookJFjHeau70BGAwsCe5FjdZ5zN0dRtQ50of/6aEol/pHRQBM3tlbT9pJsTgHQEdZ1ABTEt70vlN3Arm7QHTIaIH8PBpZIOlc1DGBpHRhB34wWH2mIsQ4ofWTr4LVOL+YGwSA6ZrR0SbR4AlFByHLJHKM025X3aENGgzlISX4y9LwZDJPa/AAGobH2zu90kAcKqZ6K92S0K+/JBBkNJhDKbtx3L9BuC5QFOv3o1DOSgSzuUNucfuU9GV8eGQ2GCuxF7K3jKMkPJqMzHp18XOIxyGhg3+Vvpyx27lidB1XrwExU13UkN7ogo4FZoXCUdJknFwPCMDSAaSivvSNFPZDRwJRINNbwcHovHCVlN2raXtJuEZQvGhmdOvgw/Qwh5gyBnvbB5YMX+47Wusno82N+lXaLoHxRqfGfGrYWHqFcjae8RxsyGnQRicaO1Los9l6LvfezG6ML67u0WwRlzeh+tHnV75DRoIv2weXKGpfFzn14ydU+uEy7OVDukNEA8Xg8HgpHr3V66x9MHq/3kCEOi507Xu9B9WegCxkNEH8+t1PVMGCxc0dr3UeSw9AWO3ek1o2uNNCFjIZyd63TK+xMOfmfg0JAk9upq0O0GwhlDRkNZS0UjlrsXGW168HAEu22AKhARkM5CoWjk4sBMta8sL67GQzTbhGAOmQ0lB1h9LljeIV2WwAyQI1/KCOhcLTROUMGms/dHMXGbmAflT0shkBGQ0akLhIqi0IR0flccHFtUnOPBUdGQ2bc2Jq9dRwndkMRoVHj3xjIaFDl9b2zt46j4wxFimqNf10ho0EmEo2RwnUWO3f52ynazQHIh3E1/k0e6kBGg0RgL3KmaZgE9D1uHlu6oUhhXQeUpqezWxY7d6ZpGKPPUNSQ0VBSfNv7/ZMb5M9e3zt0n6HY6ZrR4qEOrL0D0wmjz77tfdptAdCHvnOGicMJHbz5yzqQ0WXNt71/7uYoCehrnV7azQHQjf5r75LhjLV3YB6yt7uqYeD53A7ttgDoSdeMJoMbif9KdrSYABldzu67F651eskB3gB0DZ/8Q9/77ylvwyf/kMfVjNhnmNgdjn2GYKgHA0snGweFGUIARvS9/5774G+Ut77338vjaljXAcUnsBc5f/cFDu0GNrGe0X6eN3fzSgIyukxMLgaO1Xkw+gzMYjejk0vvklOH2AsOBiBHWzU6ZzD6DGxiNqMT6zqSpZWwrgP0xI2tkV5zJBrD8mdgGbMZneg5p5bgoR8NegjsReyt42Rwg3ZbANLae/NmrqV56OMTzGZ0HDWVQHfc2BoZfT5W58ESDmCQOJqFG7MZTRMyuvQsrO+SxRv21nGcawVGy2ldc/DlxKumr2TRnLz9h+p1PB9W5NEqZDSwKxSOXuv0ovsM5sim/0ui+clHVmUEP/nI+qrpq+DLCX1bZcA+Q0qQ0aWBjD4fq/NgYhBMppHR5kezwLDzDE2HjC4B4tFnDG6AyTQyWnnrP3xo9krD1tCQ0a3Stx+Nc8EhZ5ForOHhdCQaa3TOYPQZKMomo02LZoHR49HmrZJGRhep9sHliot97YPLZ5ufHavzcGNrtFsE5eiH2VmNjDY/mgXIaKBpLxw9XOOy2HuP12NwA8wWCQTWerqn62r7Dx/Sfc2cXpDRQE1gL/LJfw5aLnAWO3ek1t0+uEy7RVB88igEujPKz7U0P/vjGeWzkNEGQkYXF6/v3YmGAYu9l4xBW+zc8XoPjh+EXGUZrOGNjdXOR5OXLrgrDqSbA3T95n0d1zXrBRkNFCys71ZWuyxfcBUX+4SMRlca8qCR0bFIZGtoKP1Ok/f63n9v9NOzb+7c+mF2lvb7SAsZDRT4tverGgaO13mEgCa3U1cpzMlAUctpwZywonn2SsOGxx0NhWg3PzNkNJgnFI4+5ldRUxR0lH1GT3z+l6Vv2vbevKHd5NxgLziYZGF992TjoMXOdQyv0G4LlIi9N2+0M3ro4xOvmr7aGhqKRYp11RD2sIAZns5ukQ2EZ5ufYY0dFCIaCm143LNXGoSd2ekyet9XCr0Bo/rRppePRkaz6/ncDhluvvztFFZuQH72fSsr37VPfP6XolgwpyPjxjpwDgskPJ3dqqx2YYgDcpVxYYa74gCbC+Z0ZFhG+502jHWUt8Be5OnsFu1WQPHR6DKT29NTJ+damndGKdbZNI+B49Em18BDRjPF63tHBqC9vne02wJFIJsu8+SlC6udj8Ib5VVPnM66DiHOhTFrv9Mmi3blPdqQ0ex4zK9WVrtIBTsMQIOGjF3mZ388w/geE6PpXD9akrnpstXvdJCHCQ9KPTp5DeU9mSCjGfGYXyUzhDe6XtNuC7AoY5e5//Ch6bratZ7uSCBAu7H0GXcOSzYV/xP5K14EQv6svCfjyyOjGXGt01tZ7cIBVyCDLnN+dM1oSZRmsa4jmb7I6BKwGQwvrO/SbgUYK9cic+gyF87IsY4MyZrKXmR0sRO2qGD0ubRluRgZXWYd6TtnmJrnyzjTJwt0ZHTxuu9ewAB0mSikyBy6zPmhsq5D0cnGnGHREg4hfMyv0m4LGC6PInPoMheIRkZLF1KTAFauxlPeow0ZTcW5m6PH6jyTi+gZlYUsMxpdZh0Zt67DbMhoM20GwyiNVG5ikYh2Ro9+erYYi38yTuc5Q4ohjYw2Ddmicrb5Ge2GgBnIKVPCBGC6jC6KevnFCLVJIQeRaOxap5cMQKNGUmnbe/NmofUfyoNZS7vIHINQ4x9ycP7uC4udwxaVErYzymuszSj5InMMQkZDtkLhqMXOVTUMYK9KiSFV86fravsPH9JYM4fRjDz8EvuZm2z8JfZz3ldARkNmvu19sjkF84SlJLyxsfJd+5jtnGrXmJwyVSb1P40zsdjR0n1kYrEj7ysgoyGDG12vLXau0TlDuyGgj703b97cufX01Ml0y5kXWv+BtRm6+CX2852+31/vOXqn7/d5d6WR0ZBWKBy1t45jALo0kE2AwhmAstvE538pw9LMhvK/nfr+Rd31Huv1Hust7nd5d6WR0aAusBc50zRssXPH6jyo01+kIoHAWk/3dF2tu+KA6kDz7JWGDY8bA8262NldnF7p6p9peTD02dddh77uOtTcVdnSbb3eY23ptubdlS48oylvXREgo/U1uRggx3hvBsO02wK5ISWNRj89m26gea6lOfhygnYzmZbNXF8wtDq3/mTIe7uD/ysJZfGtuauypfsI6UcX0pXWMaNltUnNhozWizAx6Nvep9uSMpdrIdAfZmfnWpo1BpqXvmnb92FVe1ZU5/r2fwoubo7w820d/F9vcyeUuSy+tXQfIZ1ocsu7K63DWIe42B32sBS1SDR2+dspFEhiRDYbRki1udkrDekGmskZgKibkRPRXF+Vb2ecn2/rGqu/5/5EO5Rb+0/3Tlx5sfDQ/3ZqbKH9Fvc7IaAL6UobV+PfbMjoAgX2IudujmIAmh0aGR0JBFY7H01euqA60PzkI+vslYatoaFYBGslc/NL7Gf/26l/P69Jdn6PNHdVpgvle+5POp9f4ufbVrbHIlHJmP4/B86oPuWfA2dybRLmDCEej8d92/tVDQMWO3eycRBbVBiRayHQp6dOzrU0owpoTkgov1h42Dtx5V9P/qSc6xNn9PUeawf/10oFx5oAABWPSURBVJHXrYubI7s/bpnTQn0zWlyyw+yJRGR0ITqGVyx27vzdF6FwlHZbICHLjB799OzKd+0YaI5nN9GnGsoac33Xe452PPvcu+oKhugMAOqY0dKjwLMtza8bZHSBUAOaKdqFQN0VByYvXUCBZhnVib6MoayY6zuqy1yfXqieOasrZHSuQuHo+bsvKqtdGNxgR5aFQGk3k0XiTX2+nfHsQ7l95M/9My3TK10bwVcTix16zfXpBf3oMuXb3j/ZOEhmCFGFgzoUAi3Qzu6isKlPe6JPFsqy6+g416cXjEeXl0g01vBw+sX8W3KMN7ao0IVCoHmLREMr22NkqfL1HqvGRJ92KLMP6zrKS/vgcsXFvk9beIudu/ztFKlmB2bSLgTqrjiAQqDpkM3Wrqkm5QiGcqLvu5HzRRrKMsjoMhKJxo7Xeyz23mN1nhfzb2k3p7xkUwh0a2iIdjPZQqb7+Pm2zueXtPf1NXcfZmqiT0fI6HLh297/3d/7D17gLHbuSK27fXCZdovKAgqB5ioYWvWuusSViTSGlYe8t+fWnzx7fZ+1iT4d6TtnSMagE5vDTd51iIzW8HR2y1rnsdh7yVGEFjt3vN6DgQ7joBBoTsjauK6xeu3O8j33J11j9WSztfjpDE706ciAtXeJIktm18NDRqdz371gsXOWL7gPLvYJGY2utO4yFgKdrqtFIVBi98ctUjGufeTP2p3lB0Ofkc6yafv6WKPvueAkm0lUY300K47VeSqrXb/9e78Q0OR26ipGP3WQsRAoTpwiNoKvXi496p24ol2c6DZ3ghTB8L+dKo0B5QLpOR6dGOMQBjywPpoqYdXzZjCMBXYZ6V4ItOQHmjNuvCaVPEdet6qWV5Z1lvtnWijut2YZ5gxLE6m/cfnbqcwPhXg8jkKguVPdeE2Wx/VOXGntP43Osi6Q0aUmFI7WtL0koxkoA529vAuBCidOlVUhUHGF5aWtUfFeknS3fz35k2uqCZ3lXOma0eJthqjxT0MkGhMOIXw6W6ZzLPnJtRBo2Z44tRF8Nbf+pHP0YsaN16SSp2p5ZcievnOGNIv8I6PjokMIccxVrrLM6HI7ccr/dsq76iK7SMS7+9JtvCad5emVrp3dRdptLxE4h6VECJVFUcQuPxkLgZb8QDPZ1De90kVm+TQGlJUbrztHL6KzbBCd13VQTOmyzWjhjKuO4XLp3Oko+HJC2GySLqNLcqA5Eg353069XHpEjrXOeFifaA3Gp6T7XJIbrxmk71gHzpw12+RigJxxVdUwgPGN7KkuzyjhQqC7P26RvXz9My0ZJ/dk+617J66QMWUyfMFgheXShnUdRYwbWyPrN3DGVZZINKsWnOv7H//BciHQbE6BEgRDq6QUkWuqKePaZPGtg/+ra6qJJHK61RelvfGaQcjoIkY2ed93L9BuCNNikYhGLVCyPIP9zSaqi5GJnd3Fxc0Rfr6td+JKxq3VskUXJJH9b6fKdqc1+1Djv/hsBsOYGMxIiGbVRc1PT51caP1HsSzPEJ8CtRaYnlt/QhZaZKwMJ0vk/pkWUpBo/6cg7fcE2cJZWUWGG1urrHZZ7BwGN1RFQ6G1nu50+03IFu1iiWZi/6dg7/iVLE+BEnbxdfB/HfLefrn0yP92CsstihrOnC0akWjsWqeXDEBf6/TSbg5bhGhOVz1j5bv2IqoF+kvs58XNkSHvbbIkWeMUqK+7Dt1zf9LB/3Xkdev0ShcSufSgH100yAK7ymoXdngLyC5t4RRt2W3007PFFc0bwVf8fJtsTFm2GLml++j9gf/Nz7eRRMaKt5KH8eiicbb5WVXDgNf3jnZD6CPRnO7cqTHbuSLabxIMrb5cetQ1Vp9uPZzQicZi5PKEdR2s48bWkMsEORIwXaXmItoKuP9T0Lvqck01aewc+deTP/XPtLgnm7AYucwho9kVCkftreMWO1fT9pJ2W3SWU7FmEs3P/ngmXTQXxSnakWhocXOkf6ZFeaa1eGSZVIYTVsJhMTIgoxm1sL57snGQVLArvX50Npv69n0rC63/UI1md8WB6braoohm/9upkdetGsuWr/dYu8bqXy49QsVOUKVHRiemB7EXXDebwTBZYHfu5mhJHqGikdEkmlXPNyHRzH6l5p3dxZdLjzqfX9LYck2Kdm4EX9FuLLCOVj/a77RJTg5PnLMlCnblPdpKKaMDe5Gzzc9udL0u1aO7cyrWTIrobw0NsRzNuz9ueVdd2of1keNTV7bHMOMH2aOS0bzDanPyoip5qWV7ySV7ynsyKYGM3gyG77sXhHMIS1g2GS1EM+3GppXNEHNr/+n+mZa59SfY2gf5oTceLapk6pfGtc3pV96T8XrFntHP53aO1XnK4YCraCikkdFPPrJSP0hbu4BRxiHm29yJ3okr0ytdKIIBhSs8o1NjEgqagxTIaBFSHYks4SjV8Y3wxoZ4vwnLhUCVBYx2dhdfLDzUGGK+3mPtfH7pxcJDnD8C+kI/mj5yhnepFunfe/NGdXkGsxktFDC6zVVNLv+7d+LKbe5Eui5z+8ifR163+t/i/HUwCjKavqezW+fvvhAOuyoNO6P8q6avhj4+oToN6PrN+2wWa/a/nfr38xrtAkZkd8ni5ggqY4AJmMjo8pwz5MbWbnS9pt0KPZHCRunKgbK5qJmcF+WaahLqfKoWMBJ2l2DqD0xGZTxaspJaSGJCyGPlPdqKKKMj0djlb6fI+EYJrOIg+wDTVc8gc4DsLM/YCL6aXunqn2lRLb6sOE3V+v2LOuwuAYqwz9Bsm8Hw2eZnpIJd/2TRlGRT+mF29s2dW6qbTUgR/Td3brFwvsnO7qJ31dU/05LNGSUt3UdQwAiYgow2GwnoM03DxXhEbCwSUT2tVbhNfP4X6uVAg6FV76qLHHedMZQfDH3mmmoitfBxmiowCBlttgcDSze6XhfXKSqRQEDjcJP+w4fIFm1aA83B0Orc+pOR163ZnHj9ryd/6p24Qo6MknWQUcAIGIR6HUaJRGMND6fJYudQONronCm6zSn7vhWNWqBDH5+gtdlk98ctcspqNqHc2n+6a6yenKyKlRhQdNCPNkr74HLFxb72wWWv711Vw4DFzp2/+4JKS3IqBBqPx4MvJ+ZamtMtmyNHAuo40Ky9qY/Y/ym4sj1GQlljtbKwBqPz+SV+vm1lewyhDMUOGW2ISDR2vN5jsfcernEdqnaRgKa1hCObDSPkFO3ZKw39hw+lG2he7XxkxECzclNfPB6PREMklDufX9KoUiTsvSZl5Fa2x7A2DkqMTmMdeewF1xtTGd0+uHyk1m2xc5YLnOUL7h43T7ExGhlN9menO6rVhIFmYVPfnb4q3844P9/WNVbf2n9aO5Sv91jJKauLmyOoiQGlTY+MJiukzT6/UI6djE52ojlyO1LrpluCI6dCoGYONG8EX33/ok57U59wI6E8t/4Eq5WhrOg31pHoTZt7GLgIIxkdCkc/beEPXuDEGd0+uEyxSVlmtO4DzUrCCIYw16e6qU8I5SHvbe+qC1WKoJzpPh6dHPgwvVvNQkY/n9upahg4aE8FNLmdukpzl512RpOjWo1b0byzuzi90uWaalIWWVZs6jv63cj56ZUunE4CIDBqzpB3lN14tFBf9NzNURb2p5DqGWSLdrqMNuJkk19iP8s6y+luzd2HsakPQJveGS1U7zB9zIN6Rte0vbTYuXvcPPUC0Bset6ywkdGFQMnWvnRFMMS39pE/D3lvz60/efb6Pjb1AWSkW0Ynw7m8xqMj0VjH8Ao5tzsSjdHdPfjD7Oyrpq/SLZ7TtxDoL7Gf/W+nyNo47QXL99yfdI3Vk3194itgUx9ANrCuI39e37szTcPk8BSTX1ps37fy5s4t1S0nT0+dXPmuPRLQpzL17o9bc+tPsuksPxj6rH+mxbvqwhoMgAJhfXQ+ItHYPW6ejD6faRpeWN817aVTbQgEVjsfKc83IYvn3ty5te/T4VQX/9upFwsPu8bqtTeS3OZOkK19yiIYAFAI7DPMR//kBglo80efY5EIKW+kuuVk9kpD8OWExtMzbrwmneUh7+2MlTzJcSToLAMYChmdj81g+EbXazIMbZqdUX72SoNq5bnJSxc2PO5sFmmobrzeCL56ufSod+KKdmeZ7O5DHQwAMyGjs+Xb3j93c7SqYcDkicG9N29eNX2lWq959NOzq52Pst+onTpNta/qzcYQKeaZsbPsmmqaXunCRhIAKpDRWXkwsFRZ7bLYuZONg+YMboQ3Npa+aVM95WTo4xMLrf/IdddJMLTaPXY5m43XZNc1OssALEBGZ3b+7gsy+tzonDG6Ex0NhVY7H6meDUgOBvxhdjb7q+3sLooHMdJtvG7tP907ceXl0iNs8ANgDTJaTlybnzhW56lqGHg+t6PL9dMhG0+U0UyO087+zNaN4KsXCw+Vy5aVG68f8fbFzREU8wRgGTJajtTm/2ffwn33Aqn4bGjfOfhyIl3V5jHbubWe7ozDzcJ2Eu3B5eYubLwGKD7IaAmhNr/lQmJpXU5Pz/7EkwI3nkSiocXNkZHXrdor5O65PyGDGMPeu9h4DVCMkNESDwaWPrjYR2rzn3YM59qDzlgZIxIIrHzXrrrx5MlH1rmWZo2NJ/s/BcnKZe1tfq39p11TTbJly9h4DVCkkNEpkWisssYlFBQ9Xu/JdQmHRkav9XRPfP6XdBtP0hXU3/1xy7vqUi3sqdx7Pbf+BIPLACUGGZ3SPrgszug8avPndOJJuo0npOByxh0l7SN/xgo5gJKHjE6YXAz8z8ahAmvzZ5PRz/54ZrXzkWy4mez06xqr16ghJ2zzkxWQA4AShoyOx+PxRueMxc7ddy8U2AZxRrs+PHC/6QPXhwdIRpONJ+LhZqG2p0Yh/NvcCVLYEyuXAcoTMjoR0MfqPIUcnhINhZa+aRNndPsXFc3fV7Z/USHMGZIDSjLuwCY7SrD9GgDiyGghoPOuL/rD7Ky41JHQib7Rceh6z9HrHZX//enBtjqL9mIMoYbc7o9b+TUDAEpSWWf0wvpu3gFNaoSOfnpWdT7wm79ZWr4/rF0Z48HQZ0Pe29jpBwAayjqj4/E4N7a2GQzn9JTwxsabO7eUOwOfnjo58+97TyZbWvtPp6uMIZQrwgY/AMhGeWU0qcWxF44+GFjqn0zVjctyf+DOKK+6xvnZ1c/7+v/W2n86TWUM67+f12AxBgDkobwymtTiOO0Ytti5s83PhPu19weS+UDlvu3e/3P08cPP/p/7fykqY1SiMgYA6KKMMjpVi8Pea5WOQWtktPLok8dVv/7uetW97o9V62N8N3L+Zu9HqIwBALooo4xuH1z+4FKfxc4dvMDd/P61+Esa65rF0fzN5Yo7nR+pRnP/TAsZzUBlDADQUblkdLITrV6LQ2Nd8+OqX7fVWW61H9GOZgAAI5R+RqebD+z97QnhMSrrmv+78puGipv/9SGiGQAoKv2MzmY+UHhM+xcV1x+rr2tGNAOA+co6o2X7A7ljB1oey9c1I5oBgKKyzmjxrfMPv77hPCRe19zy/eFvrhwy900AAEggo997eurkwOP66z1WrGsGANaUdUZP19Wu8wNdY/WqmwOxrhkAqGM6o/1Om5VwqB8lJZZHRm8EXwkbuL/uVq9Ih3XNAEARwxntd9oS2cw7rDanP8PDs8lo8f6Uh+d/Iy6u75pqwrAGALCG3Yz2O21CMIv/nE66jPZ8WCEMPT/8vweav698YD/Qeq1CfAaVd1X9uQAAdJV+Rgt+if18p+/313uOtnQfFhfXD4ZWdWkwAIDuyiijJxY7SLUjYX8KxjcAgHGlk9Eej8eVXp+r93rXR+L9Ke29TRqPBwDQxdjYWCFJyG5G5zpnqG1iseMW9zssqgOA4sJwRsfjvCOx9K7AhCYj0difAgBFh+mM1ousE42uNAAUi7LIaNTdB4AiVRYZDQBQpJDRAADsQkYDALALGQ0AwC5kNAAAu5DRAADsKpGMzqnStHn8TlvBG3B0JOwJYqBN8m+NXvuVdGwS9UYpX57651z9O0L7c65sFfWPukaTcm1RSWS0rrvGdcQ7bE6ng5UGpYqepL5flPAOq83JSwqyOCg3TdEk6t8l5feE+uc8zY+J8udc9RtF96Ou1qS8P+GlkNG5Vl8yCe+wOvg4z2ZGM9Ao9VZQ/VuW3U+SzelnqnWiHxNDn/Nkq9j5qKu8fs6fcGS0QfxOm4OPx9n47CYI/1JmYkBI9UdF9+cnfvXUv5Zpf8OSrWLocy4JQWY+56JvCvWPevLjo3j93H9yyGhDpJrBwmc3iXdYrQ6ed7AwHq36o6L905NldPL3i5G+PTOfc7Um0f+cS74lrHzU/U6bpAX5/NyQ0UZITe2w0A8T2sTWd0nRCPqTCWkymuK3S/w9YeQnKGoSQ59zBr9RhOhzlOcnvBQymv5cigb6/YsE8VwFE98myW+PvL9Bh+yXO/H9ojV5qPie0P+cp/8x0fycq3RWKX/UeYfi9fP/hJdERlNfJKWBmYyW9Hoot0kx1iu5g0rrVIafKS90U/uesLIYUNkGip9zlVZR/6in2pT47BTwCS+RjAYAKEnIaAAAdiGjAQDYhYwGAGAXMhoAgF3IaAAAdiGjAQDYhYwGAGAXMhoAgF3IaAAAdiGjAQDYhYwGAGAXMhpKg7gGpN4SJXrSVcIx6KWNfEdQPJDRQBHvkERfIalkYKKp1bdMU2pa55dFRgMyGmjiHVabTVLsl9GM1jzyCBkNBkJGA0W8w2pzOh3WVOl62R+kd5LHJqryJiryio8+dSbLBksq9Uvq+PqdNpvDYUs7ciGq8ys0RVH0V1aeWPLS4sNbxC+kaIm4orCoLcLdDodDUciaiRN9wFzIaKAoMYqQHEzIlNGSUvdCMXf5V1NHXog6u8mr+J229EEnPitD9Ocs+tHiRBcFq/A0lZYovw2SP6Wej/50eUNGA0XJSEpEbcaMTnWGxf3iZG6nkoz8j+y4PcXh2mrNEaWhkJe5jHXwos6vasc71W2W3Cm8ksroPOlaI6fLFDIaKJJ1IJ36Z7Q82XLLaGVr1K6TXUbLLiG+S/wm0s6gIqnLFDIaKFKeMa0xZJAxo9XGOlSiMX1G5z/WkSGjVVoieuuppoteVG1Qhtbpt0ATMhookvcbU6mUnDpT7Vyn60drzRmmUlDzwE/FnGFcPaPFa6azyGi1Y1BF55I6HLK/qqxWm5N32hy8bEgECV1+kNEAAOxCRgMAsAsZDQDALmQ0AAC7kNEAAOxCRgMAsAsZDQDALmQ0AAC7kNEAAOxCRgMAsAsZDQDALmQ0AAC7kNEAAOxCRgMAsAsZDQDALmQ0AAC7kNEAAOxCRgMAsAsZDQDArpqaml99+eWXNQAAwJ4vv/zy/wPFgPwAKvn/mQAAAABJRU5ErkJggg==)
The second plot shows just calls to synchronize_rcu() and none of the methods scale (more on this in another post) but at least they don't dropoff as the number of threads increases, and ReadersVersion is about 10x faster than the other two URCUs.
![](data:image/png;base64,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)
The third plot shows again calls to synchronize_rcu() but this time there are two other thread that are continuously doing rcu_read_lock/rcu_read_unlock. The read-side critical section is very long, it consists of reading an array with 100000 entries, which means the time is dominated by the waiting for the readers and not by the synchronization mechanism inside synchronize_rcu().
Again here, ReadersVersion scales well because it aggregates grace periods, and so does the default urcu but doesn't have as good scalability as that. Bullet-Proof has no mechanism for sharing grace periods, and therefore, has no scalability on this kind of scenario.
![](data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAeEAAAGlCAIAAABC4dkjAAAgAElEQVR4nO3d/XMTV57v8f3zspXdUZjEEJudgZ0ZLlVhtya3KpXSHbY2UQhmTeGknJWL4SkMzPUw7DV4RUGxBrcl29gGhDEPBhzM+NkYMLEp28RRFFXfH1pq9aPUkrp1jqT3q/QD2FLrWFJ/fHzO6e/5u1OnTrUCAORz6tSpv2ttbVUBAPJpbW0lowFAUmQ0AMiLjAYAeZHRACAvMhoA5EVGA4C8yGgAkBcZDQDyIqMBQF5kNADIi4wGAHmR0QAgLzIaAORFRgOAvMhoNKKlWLi5OZpUVVVVk9Hm5nBsSXCLAGdkdD1YioWb9cip+vPWYL4lo8bXy/w/v3h5cQJ/44L50SSyFAvX5CewBGR0PSCjS2NNLmOv2je2F8chL8t447SH6Io8loyufWR0PRCV0TXKHlxViTIfMnopFralfsGEIqNrHxldD8joUjjlVjWyzJ9+tMMxC0QUGV37yOhAaB0c4x+jjieT8Yv6h83+WAPTX7r64fRT3fBt02Nzf8znjm3ri7k/qmCTTBFgPpK1h2f6bmk9R+eWF3gpzK+zJaccz2oPp7pDnrp0yLPHMXzX7aUp+sZ5enkKPMT191G577jjD+PSAvf3ruCHwfRN+3tibprb++10cLf2FDzjxCOj/Wc6L5ZiYe2j4BDSpi/pZ6n5C8YPjeUQyagp3ws9Vvskh8OFGmAfQS3eJNdumvnUMT+upM6jt5ZbXoqCGe3c5mIdUqd2Z89t68C26dQv/GJ5eNMLK7Uf7c87bg4319Y6v3cFPwzJqO2tcj8F9Gy1/gAuB3dsj/PZKhMy2nduuVX44+40/+aw+sD5E1T0sdlzz6ETbDmg9RNb+LBuP6vDiWc/Tb2dDJ5b7n5oh5fC/lgvTbLcZykWtpzx5jt4zuhCr3BhRX+zFP8IlfSOW1+lwq+a63vn/cNQrP2WEfnCB3dqTw0MBpHRvnNd7OCQXNaurstfnrb/FD6y/e5OD3Y+oLFVRQ/rrU/qcCfvZ4b3lrt+z9OvOk8dWNsbEk2aZvHMEeE5owu9wkWbU7jNBRPWfpeSP4QF30in5yvxw2D/U7PQZ6HIwV0fL/eANhkdgPzgovm9N50Bjuu/CpwehT7JZWV08XwtL6Md+1pOys7ooi9FUBltWqSXHV8x/HVu6cUFnNFFBhocnrPyd7yMfrTTKeD9w2Af2yjwaSt2cOfWup2t0iCjg5J/6x37yrbPW71ktJcfrARyZbThWHo26/8wBrhTM/3MaK/dP58z2umA7o1wzeiCL7M9aU2nQIF+dLGDF3hpnc5WWZDRwXKco8lNLdvPhWIZ7edYh/MBKxzrKDriWSrvLXe9v9PQvK05XruvuYNpIx25R4ZjS7aIDi6jHcd5Czc3/x8fxjrC+RAtnrYlfRhsv3oqHuso1h4TDxPHApDRwXIZwIuWcboWnTMsMaO9ziCVkNHO+VdRR9pzyx1/AtUh0Mpe15G/YziWNAxDZ0PavCTB4Yl8ymjLNFnx1pY8Z1i4o+A9w9zHf90+DEV+5xebMyzySSv60pYwF1BFZLTf8h0s1fGk0D5V9o9S8dPV2skwd+VKz2jHA5YyjekYAU5niK17ZHqRCvLecsfeosOr7XjEEn6PJKPNDss5wuGw9fFeZxxKyehSp7gcf4mW/447jfiWNNbh9CMY3jzz8+eeza29uXeiwDli+mS4jL0UGwYTjYz2n+mDbH/Ls7Hhobfp9ne77dhlZrStsSX38ux/Sruev+bveu5Vu7fc+aWw/ExOgxCVjsfY30Dnt9RtsN5w31Iz2uElLpyTrn33Mt/x3MCO+Qm8jzs5/hz2vz7y37D13C1vrkPX3vXgBX47F3sdRSKjq66yOTRUzh5cFQ2ZNw4vy3jgNzK62oho8TyMFMPO8aMr6QBBHSGjq4yIloHHcXSY2UfDPS3SRkXI6OriM23iNr4a+PCgcYy6tMUKjc76lvFZDhoZDQDyIqMBQF5kNADIi4wGAHmR0QAgLzIaAORFRgOAvMhoAJAXGQ0A8iKjAUBeZDQAyIuMBgB5kdEAIC9hGV1gI169spZtwyPqkwFoLIIyOr+NWDJq22Esmt+JPXcfohlAQxKT0UuxsHGHNPcdnrXvkNEAGpTEGZ3/Rn6sg6wG0FCkzWjHL9sGRgCgrkma0W5hnIx62pvnyZMnFbcRAMSTb85QXYqFXXrL7t+xiMfjfrQSAAQTtvbOusBOz1/znpbh2FJ+lZ7nDS7JaAD1oT6vYSGjAdQHMhoA5EVGA4C8yGgAkBcZDQDyIqMBQF5kNADIi4wGAHmR0QAgLzIaAORFRtc9Sx0q/b/Ga+z1Kii2PXBcH65xPEglzfP0AOMz5Uu/VHAQQFpkdN0rkNGGzcjCsSVj3C3Fwtl/Fc1oy0EqbJ63R+SfqLyIBmoGGS21VDrTfnEilc5UcAwPGa190TlkPWe08VuG7rWtUJb5C9Go66OWYuFoLBZ2OIRzN9/rw6NJY7sNj4omVW3Tn1jU+BVAJDJaaj3Dc9sO9PcMz1VwDK/96KTzZgtl9KON97GVB1+KhcPhsLHcYb5KrflRSUMpWvPTOj1VCQ83NMv4e0n7dzJqrMXIgAhEI6Or6qzyLBRR9FtH7HGBr6fSmaaD8VCkz+P99a/r/1VV1dN4dDSpuiaS5/FoY0I2m0ST1m6w4Ri5A9ofFTM2SHu89fdBvjUlPVz/YW3RH00av1T6OAzgNzK6qj49dceYrXs6hgt8vWd47r0DSiiihL5QQp8Xv7/+df2/qqraxont/ejckK5zIrk9XGM7iOoU9sZBYy1MHTPa/ijrLw39wXqf13DYEh5ORqNmkNGSSqUzOw8P6Cm88/BA2aPSyWizKSHtUZrNYeMdk9H8vxwfrrocRLXFuuleuf6usfvrsiNPwY59Mmoayi714Q5jHdmfjYyGXMhoSfUMz20/lNAzevuhRAWj0tYpvNwXbZtK5u9oXd7m8GW3g5gfofdbc/+LRrWx4uzxkvoKEuujCh0vO3ph/X3h+eHum86T0ZALGS2pvd8MG0czQhFl7zfDxR8GoL6Q0QAgLzIaAORFRgOAvMhoAJAXGQ0A8iKjAUBeZDQAyIuMBgB5kdEAIC8yGgDkRUYDQFBS6Uz7xUeVbNNBRgNAUCrfpoOMBoBA5CoM91VSW5iMBoBA/Lnvb7880B+KKDsqqC1MRqM0WuVl+2ZVTnd02hvL5YCqa1F+R5YtZMuVPQxFouG/u8++N250V3ZXmoxGacxJWjij7Vu0FLqfS0Y7ftm0K2PZEUsNfwSno+dx6It8/feyu9JktKRG9/y2/913jLfRPb8t60i2jUWWYuFoLBbObjxi2CHFvsG2Q4hpkWnYa0o7ln2TFnNGux/cmtGmXV+SDnvamo+Tf5Thh8p+w/JQ81fyXXFyGn5IpTOXRud3tw9Fuu6pqrr3mxHbNh0jZRyWjJZU/7vvJN77R+Ot/913yjqSU0ZbN/EreH/z4WwZbdusO39H+1hH0YyOGe5gfirnRuZ+AOMPZW2MvkGt5Sv0o+EbZfz5no7s3klHr0wav/VybauSI5PR1fP6TnLkN7ssveMCN3tGe7yN/GbX8+vXck/rlNGGiLbln/X+ejfZuJ24sR9t2+JbtR1bz8diGR2NGoLdti+hqZGmXQitP4l9w++o7StJMhr+uJpc1NL542O3x6Ze+3twMrp6SgroSjK6/913Ett+kXvaSjO66FiHh4zWv+Uhowtta+vYSKd7kdEI3sLK29WNlKqqD2dW95+5O/jwhePd6Ec7kDOjZ//rfNUy+rvO/8w9bT69lmJha8fUYazDdn/V4dsOYx3WQzn2ox0ObhvrsCRyWRltHOvINsz+FTIaZXq5ttURexyKKPvP3C1655tPXlXyXGS0pPwbjzYMDERjsbB18MBh6sxyf/PBrBkdjsWi1gPk7pgfsjD1qM0HLzBnqId4gTlDU7Mcf2rjgLn5K2Q0SrW5lT7Z+1Qb2Wg6GO8emvXykEqekYyW1MD72yxd44H3t4luFNDoziWmtYDuiD3WBjqC5ktGm3o+1ikff9pZmjrIaACSSKUzV5OL08vrqqourLw9l5iucIi5JD73oy1TSJ4vG/MZGQ3AFzefvNIW1XkZeg6CvxltSeUl+2hmdZDRAMqjVxN9ubb16ak7+qI6rR9dff5mtHlqv+AVwIEiowGUR68mqq3c2N0+pIw/F9ge3+cMDcVuRCU0GQ2gLC/Xtppa41o10dWN1MOZ1UrK8/uCdR0AoKqqqow/bzoY1wohVVJN1F9kNIBGt7qRyg09+1BN1F9+Z7RxqIO1dwBqgbbq+cNDiaaD8cqrifrL73Ud4sagjchoAEVNL68vrLxVVXVzKz348MX/yhWuq7CaqL8CXXsnDBkNoIDNrbS2bKOlbUB0W4rweayjlO2OAkRGA3CjjD/f3T6kFdy4mlwU3ZwifB/r4FpwAPKaXl7XxjEiXfe0gQ7JSbSuw7ZpU56e/R476WQ0AEebW+mjVybdaj1LSJqMzl+iaJt4XIpFnSsUuyKjAegmF97s6xxtOhivZi0kvwR6nWEJAx2WuvMu/WWv60bI6OBo707unfBYgdn9btnPi9ePiiQTHqgV+txgKKLs6xytsJSzELLU6/CU0Z5PUDI6OLYdAirK6FKr7OeenaxGcal0Rp8bPJeYluGClDLIUvfOQ0aXcFrWTUb/nPlJedDxc+anCo7htJ+htpFsLu3Mf/aUvJ+hvimt9e3Jb7mS20zQUGlc64wbntpe6cVt88Nk/ihsowJ3qXRmX+fokQsTNTE36KZm+tElXR5TNxl9byZ2/NqOezOxCo7hlNG2nf0K3d98ONf9DK1vUf6/ua0LjUfLHsC5H+3wTcsGtfSj4ap7aHb/mbuTC29EN8QfsoxHF5ozVI2h4omcGT2/Mt41+NEfez8o5dZ04vqHf+xtKulRXYMfTS7Gc09b6b7gejdZ+6rXfcHt/7Ft12PdmdvakyajURptblAbfX44syq6Of6QZl2HfYGdnszmVddeTk05M7r0gP7g+LXtJ643H7+2vdQHnrjenHvaSjO66FhHCRld4MmMPXr60Shd99CsPjdYNwGtBrFXlnHIQtRwoZwZfXf6Yqmd6OPXmk9cbz5+rbnUrvTg4+O5p83HWXbMwTKuZH2TbPdXHb7tMNZhOZThv4axDoch6/ydjAMw2R3CrS0ho+Hm01N3mg7GzyrPanRu0I3v1xkaT+lSJ+19I2dGl+TeTOxb5Vcnrjdrt2+VX5U/Kp2fu4vFwtb+rHnizun+5oNZMzoci0WtB9DvmfvTJ5k9jmm4IztRaByizs8x6rOG5pbobc/emTnDxra6kTqXmNaGnje30rW4tK4oWdZ1+KsOMrprcJ9tlHmf6EYBEukemm1pGwhFlKNXJkW3JUABj3WwnyEAvy2svNV3gz1yYaIuu8863+cMjX/NCvtDlIwG6tjRK5PabrBjU69FtyVwEq3r8BEZDdSfyYU3WsGN1Y2UMv68zuYG3QSS0UvJpNgpdzIaqCerG6lI171QRPn42G3Rbak2/8ej84uYxa2PIqOBuqHPDba0Ddx88kp0c6otkHUdueUdrOsAUJGbT17pc4OrGynRzRHA93od2vVh9KMB+ODl2tbRK5ONMDfoJtB6HcKuBCOjgdo1NvV6d/tQS9tAg8wKFsa6DgCyWN1IHbkwoQ1u7D9zl4xWyWgAktjcSmsl+VvaBrqHZkU3Rxa+j0frBSxFDnaQ0YDMUulM+8VHlm7y5lb642O3G3Zu0E0w9TqylXKoqQTAQc/w3LYD/T3Dc6l05qzybP+Zu7W4G2x1BFL3jrV3ANyk0pmdhwdCkb4dhxK7cvsN1s22Kb7zeTzaVDGStXcAbHqG53YcSoQiSugLJfS58umpO9PL66IbJS/mDAFUT64TrWi3ptY4izcKI6MBVMnCytvP/jy+vTWhZ/SOQ4me4TnR7ZIaGQ2gGrqHZpsOxt/LpbN+2/vNiOimSY2MBhCs6eV1fbvujthjBjdKEuh+hsKQ0YA8Ws8/0LbrZvFGGfyvTSpDSJPRgHDTy+va1SgLK28vjc7TfS6P7/1oC9ZHAw1ndSOl9Z0jXfdEt6XmMR4NwE+XRucbuSS/79grC4BvriYXtbnBSNc9ym74gr2yAPhmYeVtpOteI5fk9x17ZQGoyOTCm32do/s6R0U3pD6xVxaAMm1upU/2PtUGNz49dUd0c+oTe2UBKMfqRmpPx7BWte6s8oyldQFhXQeAcrxc22ppG/j01J2Flbei21LPyGgAJegemm09/4A1G1Xjd0ZnC0iLvIBFJaOBAEwvr3966o42+kzfuWp8nzM0BHMyKmpEmowG/NU9NKul8+72IZbWVVMw+xlmsfYOqBPapYPMDVaf/3tlmfrRgkY7yGigcptbaWX8+eZWWlXVl2tbjG8IEXRNJTED02Q0UKGbT17tbh8KRZRziWnRbWlorOsAYLK6kYp03dOvTGEJh1hkNACTIxcmtKp1yvhz0W0BGQ1AVVVVXd1IafOBD2dWzyWm6T5LgowGGl0qnTmrPAtFlNbzD0S3BVZ+ZrTTjCFr7wCpaVXrtNHnS6PzopsDK18yOhvODhes5C47rHJUk9GAFzefvNLSmQ1hpcVYB9C4xqZe7+kY7h6aFd0QuCKjgcayupE6cmHiyIUJ0Q2BJ4x1AA2ke2i26WA8FFH2dAyLbgs8Yc4QaAipdObjY7e10eeO2GOW1tUKxjqA+pRKZ9ovPtJLIC2svNW6z1Stqy1kNFCfeobnth3oj8aedA/N6nWRRDcKJQu6phJjHYAAqXSmuS0RivSFIn2hiMK6utoVYD9a3LbgZDQa3b+duRv6QglFlPe+UA7+5b7o5qB8gY51UOMfEGB1I6V1n7XbzsMDFOavXUFmtHnnrGoio9FoUunMpdH51vMPNrfSPcNz21sTekbvOJToGZ4T3UCUKdjxaFGbzpLRaCjK+PM9HcNaIk8vr+/9ZkQPaO2295sR0W1EmYSt68hvIO4Q5EuxsPGSGEP0exvhJqPRIIyrnvd1jrKurv74nNHGrcALDXXkv2ffPDwZbQ7HksYJR8tWtsWR0WgQqxuplraB3e1DV5OLotuCQPg+1mFMZddNZ41LPpyXfyyR0YCzl2tbHbHHezqGWe/cCPzOaFOYuq7rKDmjSxvqIKNRn1Y3Uid7n2ojGy1tA2R0Iwh4rMMlU0vMaMfDF0JGo/5sbqVb2ga0gD56ZZKCGw3C9znD/FxggWUd5Wa0++iJGRmN+rO5lY503TtyYWJh5a3otqB6BK3rKDRnmLuD/csFeuZmZDTqgzL+fHf7UNPBOL3mhiVs7Z0+xpwN3Xz+mhZZR5PeeuZmZDRq3cOZVeOiOi4UbFiBrusQhoxGTdPqiIYiCovq4P+coQwhTUajFr1c25peXldVNZXOdMQeXxqdp/sMapMC4q1upI5emdT6zgw9w6gK49ECqt+R0aghJ3ufansMhiLKyd6nopsDuZDRgEiTC2+0dG49/4BFdbAjowEBbj55pU8GXhqdfzizKrY9kBYZDVSVcVEdQ88oiowGqiSVzuw/c1dfVHfzySvRLUINIKOBKtGGnlvaBlhUB+/IaCBAqxupjtjjjthj7b+TC282t9Jim4TaEkhGLyWTYvYDzyGjIdzmVvpcYlpbVNfSNiC6OahV/l9nmC/C4VK7rgrIaIi1sPJ2d/sQi+pQuUBq/OdK/QsY5dCQ0RDr4cxq08H4/jN3WVSHCvmb0dmeczaj6Uej3qXSmfaLj7QJwLGp1x8fu/3xsduiG4W64vt4tLFkh6CEJqNRLT3Dc9sO9B+9PKkvqiOj4S9h9aMDRUajClLpzM7DA6FIXyjSx6I6BISMBsrUMzy341AiFFG2Hej/tzN3WVSHIARRm9S4VSFzhqhPL9e2mlqzxepCEWXn4QF60AhCEOs6ktFs4WgyGvVpenn9w0OJ0BeKntE7DiV6hudEtwt1KJC1d2p+e3AyGvVmbOp1S9vAe5F8QGu3vd+MiG4a6lCw49HJpJiNs8hoBGRy4Y126WBH7DGDG6gC5gyBEoxNvQ5FlLPKM9ENQaMgowFPuJ4bQviU0closz5byJ6zqC+pdCbSdS8UUZTx56LbgoZDPxooZHMrrW2b0tI2QFca1UdGA65WN1L7OkdDEWVPxzABDSH8zmjraAdjHahhV5OLWgmOl2tbotuCBuX7dYbC6igZkdHwRSqdufnkFRd5Q6CgrmERi4xGJa4mF/d1jlL6GTLweaxDXMloEzIaZTvZ+1S7bpB9uyGDIGoqMR6NmpRKZ45cmNAC+mpyUXRzAFVlXQegU8afhyJK08H42NRr0W0BsshoIGth5e3RK5PTy+uiGwLkBbpXlpiBDpWMRilerm2dS0yvbqRENwRw4Pues4ZgFrcSj4yGR9PL6y1tA1znDWkFuvaOGv+Q2s0nr7RCo5GuexQahZzoR6NBaVVGQxHl6JVJ0W0BXDEejQY1NvV6d/tQ99Cs6IYAhbCuAw2HCwhRQ8hoNJDNrbRWCZpLVFArfL/OUNj4hhEZDbvVjZRWCXp3+xCFRlErfO5HSxLSZDQsVjdSezqGQxFlX+cohUZRQ6jXgYYw+PBFKKLsP3OXQqOoLYxHo1EwVYhaREajnnUPzba0DVAjCbWLvbJQt45emdSuUiGjUbvYKwt1KJXOtJ5/oBUapVQ/ahp7ZaEOaTOELW0DkwtvRLcFqAh7ZaEObW6lzyWmWQSNOuBLRi/FwtY1d4xHo/qml9ePXJggmlGJnzM/KQ87fs78JMnRWNeBOjE29VorNMp13qjE/ZlLx6/tuD9zSZKj+X+doT7WYS5UWlVkdKPRtiIMRZSO2GPRbUEN+znz05/6//nE9Q//1P/PlXelfTlaoPU6hF0ZTkY3FL0S9LnEtOi2wBN/xxNKsv7Dq6XvJ7Tb/Mp48tl5/dY7fvjc0L+cuP7hievNJ65/eG7oXy4lP6vkph/tW+XXZXelJdqHJT+q7fCgpVi42fu6PjK6oUwvr3987DZDHDXEr/GEF2vf6YE7uRjX0/bW0y5jVp5Wdv2x9wMvt2O9TcevNZ+43nz8WvOx3iaPj/JytLK70gGPdXhf5JEfGbEvsk5Gm8OxZAlLRsjoRrC5lR58+IL6GzXHcQQgld7U03bp+4m70xf1wI1PdOpp2z3ySYW5WSxSd5y43qzdjl/bUUlMW45Wdlfa9zlD4xqP0jrRxnB3SONSlvWR0XVPLzR6aXRedFvglRbEV+4c8CUEy7idVnYZ+9fGgY7vng98q/xa6/bmmld+51f7PeTL0WRZ10FGw7vp5XW90OjqRkp0c+Bq/YdX2pjvlbEvzyZ+5+94QtfgR3ra9o4fNgbu/Mq43iV/++Oal6ben7n0rfJrPVIr7Pz6eDQyGjVmYeVtS9sAhUbltLa5OLV8QxsOdhwFdhtP6B75RA/c+ESnnrZ3py8ax0CCm2b8y9A+x98EfxnaJ/ZostRUIqPhJpXOtF98lEpntP9q13kfuTChfwUCvVj77tF878jk6UvJz7x0fk9c+9Cv8YQGIU1NpUJzhrk7kNENqWd4btuB/p7hOf0rdJ9F+Tnz09L3Ew9mL8cnOntu/cHj+O+tp11TyzfWNhf9HU9oEBLVVNK74Nlj5NeFmDrnXnrmZHTdSKUzOw8PhCJ9HxzsD0UUZfy56BY1Fm2WL/nsfN/9r/964/dFQ/ls4ndXxr7URoTXf7BWHPR3PKFBUFMJUusZntvemghFlNAXyi+/6GcvlaA5zvIVuP31xu/77n+dfHZ+6fuJVHpTdPPrEPsZQl6pdGb7obh2DWEoonz4HwnGoL3zeC1f0Vk+y63n1h/iE50PZi8HOoMHnSzrOvxFRteHnuG597/s1zN6x6GEcVQahbldy1fqLN+l5Gcjk6cfzfe+WPtOyA/S4PzuRwsqomRBRteuVDpzaXS+pW3g0uj83m9G9IDWbnu/GRHdwNpgvJZv4fW9smf5RP8cCGA82jTvJwgZXaMGH77QLk4JRZSTvU9FN6eG3X567uT1Fi/X8hWe5YNwQY115EamxWQ1GV2LzirPtHT++Nhtdokt29TyjctjBwpcy8csX20JKqPNW7NUewSEjK5FV5OL+8/cZXVdedZ/eHXraZe2EsNyLd+J6x9evBlmlq9GBXadodCBaTK6Jrxc29J272ZYoxJTyzeujH1pu96aa/nqRKA1/oUhoyW3uZU+2ftUG9loOhgffPhCdItqz9sf15LPztuXMH+r/FobieZavvrA2jsIoHWfta2tKFxXqvmV8b77X9tn/66MfTm1fINr+eqMvxmtb7ySHY4WtbqDjJbTw5lVrdTG4MMXRy5MTC+vi25RLXn749rd6Ytdgx/ZF2bcetrFkox6FUy9juyYB/sZIktfVHf0yqTottQet45zz60/TC3fEN06BCuQ8ehcVJe2n6GPyGh5TC680TZM0RbV0Xf2LpXedOw4n1Z2DT4+zgUmDSKYa1j0AQ9Bgx1ktDz2n7kbiih7OoZZVOfdi7Xv4hOdJ6432zvOk4txVmg0FOYM4b+FlbfnEtMv17ZUVZ1ceHM1uUgtJC9S6c1H8732EqB0nBsZGQ0/rW6kjl6Z1EY2uodmRTenZtBxhhsyGr5Rxp9rOw1qi+rYLaWonzM/0XFGYb5ktCyXrujIaCE+PXUnFFFazz9gYrCotc3FwcfH7fWa/3rj93ScYeRvRle0V5aPyOiqUcaf7z9zVwvl1Y3Uwspb0S2S2s+ZnyYX4/YaoSeuN8cnOinQDDt/xjrMFZTYh6Uh3HzySl9Ud2l0XnRzZFeg4/xovpf6c3Aj0Z6zPiKjg3bkwoSWzrvbh1hUVwAdZ1SIOUOUI9J1r6Vt4FximkV1bozFQuk4o2y+Z7Rx21lhE4lktO9WN1JapbqbT6gLkee4r6u9WCgdZ5TN95pKhmBORkUVVSKjfbS5lT6XmNYX1ZHRRsZ9XTns+I4AABBzSURBVN06zl2DHz2YvUzHGeUJdDyaeh31QFtRpy2qY9mGkb6v66m+X10eO2CvedR3/+v5lXHRzURtox+NrFQ6037xkT6+PLnwRvvH0SuTrecf6P+FbnTyjOO+rl2DH92dvvj2xzXRDUQ9YDwaWT3Dc9sO9PcMz41Nvd7XOcqwhpul7ycGHx/vGvzIvq8rHWf4jnUdUFVVTaUzOw8PhCJ9vzzQry+qY2TDSItmfbjZsq/ryb6dd6bOi24j6hAZDVVV1Z7huR2HEqGIEvpCaToYZ1GdbublrfhEp30mkH1dUR1kNPROtKLdWtoGCGgtmu2XBWrDzZdu//upvn9iX1dUAfsZNrRUOnNWeRb6XPngYFzP6B2HEj3Dc6KbJsDPmZ+mlm/03f/aLZpHJk+/Xp9RVZV9XVE17GfYuBZW3mrr6t7LpbN+2/vNiOjWVY8ezfbyzdplgbeedmnRDFQf+xk2qLGp100H49ouVo25ri6V3pxcjPeOH3aL5uSz81RwhnDsZ9iguodmG7MSvx7NjoMV3SOfEM2QCnOGjWVy4Y2+6nl1IyW2MdX09se1R/O99jIaejQ/mL28/gPrwSEdMrpRZKcHI0ooojTOsg0tmi8lPyOaUaP8zmjjZYbU+JfGy7UtbXqw6WC8Eerxr//w6sHsZXvVZu12KfnZo/lertVGTfB9zlCKIv9ktMXu9qFQRNnXOVrf04NaNHePfEI0o26wD0tDOHJh4uiVyXqdHlzbXEw+O+8Wzb3jhycX40QzapT/6zpkSGkyWlXVyYU3+8/cHZt6LbohlXKso6/movmvN35fIJqp2oxa5/tYB3vOimecHjyXmBbdnEoZ6+irqvp6fWZk8rRjNJ+43tx3/2uiGfWEdR315uXalr5dd/fQrOjmVEqvo39a2TX85FTX4Edu0Ty1fIOSRqg/ZHS90XrQdTM9ePvpOcc6+n/s/eC0sotoRt2jxn/90K5JWd1IXU0u1vr04M+ZnyYX45fHDtjr6J9WdsUnOmde3hLdRqAa2CurHoxNvd7dPlQfNUXXNhcHHx/XKs9Z6uifuN6ceNgpuoFAVbHnbG1LpTMne59qo8+Rrnuim1M+rYyG5aoT6ugD9KNrWCqd0aYHmw7Ga3d68MXad/GJTnvxuT/1//PJvp3U0UeDYzy6hqXSmZa2gRqdHkylNx/N9zouodN2bqWOPqCyrqMWrW6kziWma7dq3fzKuGNB/b/e+P2D2ctcEAgYkdE1ZmzqdUvbQCii1FxppLc/rt2dvmhf4HzienN8ovPF2neiGwjIiIyuGal05uiVSX16sIZW12k7UTnudfJovpdrAoECfMro7PQg14IHSLs4pYaKi67/8OrW066zid9Zovm0smvw8XH2OgG8oB9dMyYX3hy5MLGw8lZ0Q4rQtnB13PGk59YfJhfjLJ4DvCOjpba6kTpyYaIj9lh0QzwxXn5ivJ1N/I6OM1AeYRmd3Z3WaTjE9i3DEIq3Bdf1kdE3n7zSpgf3dY6Kbksh2nXbjpueXBn7cmr5hugGAjXMl4zOh6qNy3h0/mIX24UuDt8qeeuAOshofXqw9fwDaacHtctPHDvOt552sU8gUDkx/WjjVgCWbQGcvtVwGb26kdKmB5Xx56Lb4qDw5Sd0nAEf1UhGlzbUUasZnUpnHs6sav+eXl5/ubYltj128yvjjtdtdw1+dHf6IpefAL6riYzWea0AUosZrW/dLeHSOrfLT/TrtkU3EKhbYsajy81oNRn1tOK65jJ68OELbXpwd/uQqNV1jtsGatdtc/kJIIqgdR2lzRkav1WH/ehLo/P61YMCq3AYtw10u/yE67aBKhO29k4fY86GriF/Ld8y9tI9XrZYWxl9LjEt/OpBfdvAU32/ujx2wN5x7h75hI4zUH1cwyLM1eSiPMs23LYN5LptQCzqdVRVKp1pv/hInx7c3T4kukXq/Mr4/4y32bcN5LptQAb0o6uqZ3juvS/6mw7GtYDWV9pVn3GNs2XbwJPXW25+92dRDQNgREZXj7ZtSijSF4r0ffZ/x0VdPbj+wytLVQ22DQSk5d9YR0nXggdMzozuGZ7bcSgRiigfHIz3DM9VvwEzL2/Zy9Edv/ahZc9Atg0E5OFTRmtrL4TtX2glW0an0pnzAzMf/kdCW2MXiig7Dw+k0plqPfvmg9nL9itQtIsDHa9MYdtAQBK+jnVke9OCNgM3kCqjU+lMpOte6HPllwf69YzecShRha702uai46XbV8a+nHl5K+hnB1C5IMajcwMf4rrV8mT05lb642O3QxFTQGu3vd+MBPe8U8s37MVCWUgH1JwA5wyTUcajVW2N3Z6O4eoUSNIKa9ivD+TSbaBGBZDR+nWB4sY8JMnoVDqzp2O4OgGtlXKm5hFQZ/zM6Fw4Mx5dPW57oJxWdlFlH6gDrOvw38u1rX2do0FvcPX2xzXHskfdI59wfSBQN1gf7bOXa1t7OoZDEeXTU3cCeoql7ycc64VSkQ6oP1xn6Kfp5XWtDPTHx277fhmhNqxh36HqbOJ37IEC1Csy2k/7z9zVykD7e33K+g+vRiZP27d27bn1BzYPBOobGe2nm09enVWe+RjQ8yvj9qu3tUL7LHMGGgEZ7YPp5XV/K/RrRekcr95+MHuZZc5A4yCjKzU29VqrNTq9vF750dY2FwcfH3e8eptlzkADIqMrogd0R+xxhYeaWr5hH9Y4rewamTzNMmegYZHR5Xs4s6oF9Mnepx4fYt97O5XedCw+p129zTJnoMGR0eXT9vM+l5j2/hDj3tuv12cci9L13f966fuJwFoNoJaQ0RUpqRCHYe/tf+q59X/sy5xvPe1imTMAIzK6ZN1Ds7vbh8amXpf6wLt/63bce1vb3TWAlgKoeWR0ac4lprXqzyVldCq9eXvq3LHe7ca9t7Vlzly9DaAAMroEekAr4889PiSV3kw+O39a2WXde7tv552p80E0EkA9IaO9Gpt6XVJA6+nM3tsAykZGF5FKZ9ovPkqlM6sbqSMXJm4+Kb5U2ZLOuYC2br/N3tsAiiKji+gZnnvvQP+f+/7m5c72dNYu4D4T/w17bwMoAxldSCqdef/L/lCk7/0v+wtXSnJLZxZsAKgEGV3Ip6fuhL5QQhFle2u8Z3jO8T6kM4DgkNGuzg/MhCJ92jxhKKLsPDxg6UqTzgCCRka7ajoYf+8LRc/oHYcSeleadAZQHWS0q998dSMUUX554Npn5/7wywPXQhFl7zcjpDOAaiKjrR7OrHYPzer/1asgkc4Aqo+MNtHrQWvFkvQqSCf7dpLOAKqPjM6bXl63FOx3rIJEOgOoGjI66+XaVkvbgB7Q6z+8Gn5ySr+AW6uCRDoDqDIyOksrxxHpure89rf4RGfuAm5TFSQu3QZQZY2V0aN7ftv/7jvaTQn9/V/+8z0l9Pf9774zuue3qqrenLxl3FGQKkgAhGusjO5/953Ee/+o3Xo+33bsf5p6Pt+WeO8f//t//0P3yCfGKUGtvjNVkACI1aAZHX//FycvfXDi+ofHrzSdumAtdRSf6PzzwF6qIAEQrkEzuufzbSeubrcs2DhxvXlk8vT6D8WrjwJAdTRiRmudaOOCjZP//X7y2flUerO6zQSAIhoxo3s+33biyvb8QPPV7Rc+C1W3gQDgScNltLETrS/YOBH7gAUbACTUcBlt7URrtyvbb906Wd02AkBxjZXRA+9vO/X/3ndcsPFt9wdbL15Ut5kAUERjZbTd1osXg9s/0K5qubl3T3qTaUMAEmn0jFZVde3B/cS2X2gxPR7+NJNKBdYuACgNGa2qqvpiIKFfI/6o7VAwjQKAkpHRWbP/dV6P6b/96dsAGgUAJSOj877r/E89pp9fv+Z3owCgZGR0XiaVuv/v/6ZldGLbL17fSfrdLgAoTW1k9FIs3KyJesrNsvfKSm9u3v7Xff3/8Pd6h9p400qYlspYELXyo/lL2rZJ2zDaRtuqfLRayOilWDibzcloczi2VPwRlew5u/XihbGEqfHW/+47ZRzQ36P5S9q2Sdsw2kbbqny0GsjopVhYD2bjvwuoJKNVud8wf0nbNmkbRttoW5WPRkY7kPkN85e0bZO2YbSNtlX5aGS0gwIvcXk3f4/m703atknbMNpG28o+WhlxVJ8ZPTAwEK9A1d4wGW7Stk3ahtE22lb20coIwBrI6DLmDCvk70vs79H8JW3bpG0YbaNtVT5aLWS0qiaj2aV31Uhoud8wf0nbNmkbRttoW5WPVhsZXWUD729z/Ptl4P1two/mL2nbJm3DaBttq/LRyGgAkBcZDQDyIqMBQF5kNADIi4wGAHmR0QAgLzIaAORVbxldaqXpaluKhat1JU5J9KuEZGqc9cWq8qVMBdnfSFlaZ2+HPCeF82skwUlhb5g8Z0R9ZXTVrxovVTIajsWi0rUsXwYl/wqKlow2h2NJU62WqCxttLVNgjZl2V8leU4Kl3dQ/Enh+KJJc0bUVUaXUX2pqpLR5mhSTUqe0TK1zrk5orNGUwOftnBsSc5mGt5BuU6KXMNkOiPI6KpZioWjSVWV5uNoov85LEVnUOf4Lkry1hqbkf/DWJqXMNc8GU8KUwLKdFIYXiB5zggyukry7ZHk42iWjDY3R5PJqPjRNxOHd1GaN9aS0blTWbZOvnwnhVPbpDgpTC+PPGcEGV0d+WkbqTpbGnlfN1tr5EhAVVVdM1qGF9D4Ksn25hraJtdJIe2LVlcZLdH0SAFSdBlMjNMicr1wpvNjKRaWtW35V1D4FJPtVZLopHB/BwWfFNaGSXVG1FdGy7MAqgD5MtrUo5GlcbYhXtMXxDbTYfhZlvVtTq+SLCdFgXdQ7Enh0DCJzoh6y2gAqCdkNADIi4wGAHmR0QAgLzIaAORFRgOAvMhoAJAXGQ0A8iKjAUBeZDQAyIuMBgB5kdEAIC8yGnXDUCHUd9kaO271dQJ66iB/ItQIMhpiJaOm6KsklQJMNKcClS4FpH1+WjK60ZHRECsZbQ6HTdV6Jc1o26HJaFQDGQ2xktHmcCwWbc7Xobf8w/xF7b7ZOs3Zur/G7Utjubq/pvr7psrOS7FwOBoNu45cGKoJ602xlRK21Bc2PbVxSxbjE9laYqxbbGiL/uVoNGorTy26QDWqjYyGWNlRhNxgQrGMNtWt16uxW7+b31jD0NnNHWUpFnYPOuOOHIZ/e+hHGxPdEKz6wxxaYn8ZTP/KP57+dAMjoyFWLpKyUVs0o/OdYWO/OJfb+STT/mPZMs+2U7ZTcwxpqOdlKWMdSUPn17Hjne82m76oP5PD6LzWtSanGxEZDbEsHciY/xltTbbSMtreGqfjeMtoyyGMXzL+EK4zqCR1IyKjIZZ9n+gCQwZFM9pprMMhGt0zuvyxjiIZ7dASw4+eb7rhSZ0GZYTvaYtqI6MhlrXfmE+l3NSZY+farR9daM4wn4IFtxG1zRmqzhltXDPtIaOd9jHNP1U0GrX8qmpuDseSsXA0aRkSIaEbDBkNAPIiowFAXmQ0AMiLjAYAeZHRACAvMhoA5EVGA4C8yGgAkBcZDQDyIqMBQF5kNADIi4wGAHmR0QAgLzIaAORFRgOAvMhoAJAXGQ0A8iKjAUBeZDQAyKu1tfXvvvrqq1YAgHy++uqr/w+oyIMkvXF+GQAAAABJRU5ErkJggg==)
Here is the implementation in C++:
https://github.com/pramalhe/ConcurrencyFreaks/blob/master/CPP/papers/gracesharingurcu/URCUGraceVersion.hpp
Not only is this code simple enough to just copy/paste into your codebase, but it has a pretty good throughput and scales well.
This seems to be the best basic implementation of an URCU (with just atomics) in all possible scenarios. There may be other implementations that surpass it, but they would definitely have to use some non-portable trickery, which URCUs usually do, stuff like using POSIX signals, or some CPU special instructions, or some Kernel API.
Anyways, if you're interested in portable low-overhead memory reclamation for C/C++ that is wait-free population oblivious for readers and blocking for updaters, yet capable of aggregating grace periods, then this should do the trick for you!
We did a simple benchmark against two of the URCU implementations in the userspace urcu lib http://liburcu.org/ and we'll make the benchmark available in a couple weeks, but in the meantime here are some plots below.
We compared against the Bullet-Proof URCU and the default URCU. More details in this readme https://github.com/urcu/userspace-rcu
The first plot shows just readers, i.e. calls to rcu_read_lock()/rcu_read_unlock(), where the read-side critical section is just an atomic load on a global variable. This means the read-side critical section is as short as possible, and that if there is any contention is caused by the overhead in rcu_read_lock()/rcu_read_unlock(). As you can see from the plots, all three scale well, but the ReadersVersion is the one that scales better.
The second plot shows just calls to synchronize_rcu() and none of the methods scale (more on this in another post) but at least they don't dropoff as the number of threads increases, and ReadersVersion is about 10x faster than the other two URCUs.
The third plot shows again calls to synchronize_rcu() but this time there are two other thread that are continuously doing rcu_read_lock/rcu_read_unlock. The read-side critical section is very long, it consists of reading an array with 100000 entries, which means the time is dominated by the waiting for the readers and not by the synchronization mechanism inside synchronize_rcu().
Again here, ReadersVersion scales well because it aggregates grace periods, and so does the default urcu but doesn't have as good scalability as that. Bullet-Proof has no mechanism for sharing grace periods, and therefore, has no scalability on this kind of scenario.
Wednesday, September 14, 2016
A simple Userspace RCU in Java
In this post we're going to talk about a simple RCU implementatio, and provide an example in Java, but it's easy to port it to C++.
So, what is RCU again?
Yeah, well, RCU is many things and it has been talked about it detail by one of its original authors here.
... no, I mean, like, what is RCU, like _really_?
In its most basic form, RCU is a concurrency construct to provide on-demand quiescence.
By concurrency construct I mean it's a building block to use in a concurrency setting, the same way that a mutual exclusion lock is a concurrency construct, or a semaphore, or a reader-writer lock, etc. And by on-demand quiescence I mean that you can introduce quiescence anywhere in your application, as needed (more on that later).
RCU's basic API is:
with the first two being called in a block, i.e. for every rcu_read_lock() there must be a later rcu_read_unlock(). Also, we can not call synchronize_rcu() from within a block of code enclosed by rcu_read_lock() / rcu_read_unlock().
Now, unlike the examples I gave of locks and semaphores, RCU does not provide any kind of mutual exclusivity property, at least not directly, but it does provides two very interesting guarantees:
First, the effects of any operation that happens before synchronize_rcu() in one thread (T1), will for sure be visible in another thread (T2) after rcu_read_lock(), if the call to synchronize_rcu() in T1 returned before the call to rcu_read_lock() in T2;
Second, the effects of any operation that happens after synchronize_rcu() in one thread (T1), will for sure not be visible in another thread (T2) before rcu_read_unlock(), if the call to rcu_read_lock() in T2 started before the call to synchronize_rcu() in T1.
... yes, it's confusing when you read it for the first time, but the idea is pretty simple. Here is a schematic that may help to understand:
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2lBYn88w05bvyOWvdrcFz6/byzf0co/m8TbvwNxY2/9Yj/TarIv3MV+fe4suSTAvTOgScPbC78nH9Ia3ce9VGAdpUkUYAuC8btZwrQhBBCiL0of5jSgQYAAHCbGzfkuLHqj8hNOM7ekFP8ykwVe0OO23+oUN6BY+MPKpV8Q44b32ZjegeO7T9MisTqt9n4/4YcN77NxvQOHL+VBhjTD2NSgHY67hWgm8+oP00Ir+wrEBsF6JabI+vK55RfAbrjclw5brz7Iwf3WTO4YFlW7Eg1r+b8/b2mI5/oCsfVFdqyK0D/1QPVk0V74TrbLbR6tJtXqGEBuqa+cXdfZFW1cTkTf3T4hI0Nnbmg+r+r1WsdpXhxa/uj7k1dWYYCNGMyJmMyJmMyZgnGLEu+KEB/OzpScOXnjXhi8drkyNHB67su59aKvvZ/37p3embuSWpD/3k/rT8Yc7gDTQFaSZIoQAefcfU5k8lkMhnPC2SEEEJIEJP9MWpcg/b6gwAAAAAAADBh49ts/H9DjhvfZmN8+41LN+QY3zyj++fPkZHR4eHRhw8f37//73fvDv/444Pbt+/fuvXjzZtDg4O3rl+/0dc38M0333reAPNl3CtAN+69s6h68dYmm+0UHPXSfHtMObJpZTa/L1uGBWjtur+Ljy86t8+aTq22z6qb2o7Hs0JHql7EdyNy2uQ8aT48vKxtEpRdAbrm/FNV53x9qkXw8jn3RHXhLT58t8AjXbxCzQrQui/iavT2r6zvgOa0n7hzpgQvbvO48nYKa+tzV2YoQDMmYzImYzImY5ZgzLLkfQH68tC1lG4DZCO2OHFioNf46f/xh9HB1XWdGvRPqW+GKEC7RZIoQAecQfs529laX19fX1/3vEBGCCGEBDHZH6OaGjQdaAAAAAAAgNIw+PPn2tpa7s+fS0tL8/Pzs7OzMzMzk5OT4+Pjo6OjnjfAfBkXC9A1rfcnVK/eRmS4q9aZwdWV2fXJZqMHt52eTuedSmVYgNZWZjdiHS1O7bOmy2u+AnTdF4+mNX/nL3ikpzpURemNx9+dMhg5f/Fg509dVybN6il3sT+hmpaJO2cFntXWMauqkxtNpotXqGkBurGmvuWDkbwO9PTgbosbqu2LKAfJTA86dQjCr2yso9ntLQY+nV98+W9/6j5/4asvL1651B3u/era5SvXRQrQ/QNDmu/AKf7bbES+Fcfqt9kE5RtyxL/Nxt435Dj1bTamg9j4NpugfENOUEqWjMmYjOnPMcuS1wXo8OklvUWcM6vXHoov4dz710+XFvIqJJnUzHsUoN0hSRSgg0yw/ZxOpz0vkBFCCCFBTPZzIR1oAAAAAAAAT1CAdjpuFqDrm1sm1tUvYPphX5sTI3dcVq+Oe+dKc4FHtrWMJ/T+ZF2OBWhtL1POxB9/0ODIyJqOsvHy0s2/ujmrXXHb8Eh/fjOmfuTjwwVWAtZvPzt/6rrxclg+5d69qzrL5Y3lcEeh8/z5zGuXVTZZ1Nm9K1SkAN2od3PCRmTkyzpL29KsB2++gnjR+ejhtHKLpivQk/rG0dHR8fHxycnJmZmZ2dnZ+fn5paUlkQI0f2oBHOTgt9kYPN32t9kE5RtyRL4Vx9435Gi+BscgImMWeu79B490IzJmoecWM+YPQw8sRWRMzZ0zphEZU+TmGUduyCny5pmy5G0BeufIQiJ/nzLxs2YLP+ssBX03GtV+rvlpcuo7CtBukCQK0IFl3H7OVZ+zvwT0vEBGCCGEBDHZ38Qpa9B0oAEAAAAAAEqGArTTcbUA3Vjz0f0JbRE2PT78zR7DhmLtqUvnJqLd+o3J5zk2mlKNujym0/Q98YeOSCrXB81sKHelLAvQjT+/OqPpB2dWZz77tKX4kdV9VlneWOz+WKeMW3vqm/BcSq9xbnikzT88Vp0nui3Ylg/uLmwe3cZyZFlscM9SbAG65kR4SNNDy8T7LxYqKLd9+DiubhMLtJldu0LFCtCNNSe6wtqyvNWFqM9cUDXWDG6HcCaaYvr0XZGVuSs9FKABAIA9nhagf7iWytuhTPyTb20OuGcsrq1Tbyz+/jIFaOdJEgXowBJvP6dSKc8LZIQQQkgQk0qlxDvQXn80AAAAAAAAKDcUoJ2OywXo+sZ37yzm12Ez68vDw98f/7R9V6613NC299Pe1rtPRpfSaVk2akxmc+7JombM5Hz4q87sgLVNfzh+69lUcrNYmYk/OjOibGSWZwFab0ldWZY34ovT4cHeA/+7Tdkqrm36eO//bj9wvr994GH/ZHRiJZWaGiw4smbVW1mWM6s/3ur9L03NNfWNNSdO/XnnQHh6OaXssq4t/Pu8YoFhwyPVFqzljfjck3/pbKurb6w5ceq/nP/xxxXleZR+2Nd2WrlDnhWgtatuW2d0quc32mU5szT/7Nz5zj9vel7zrftd5/HBJ6NJ7XW2Oj24W+AQXLpCRQvQ9Y01LYMPteuNWluIeu8d1ZtBJvrDz118xc92q7YW7/7IkxMvYKEADQAA7PGwAP1n48t5n5MzI2N9RYzZ+2FM+z9rMzPOLAJNAVpJkihAB5Zp+3ltbS3bfk4mk54XyAghhJAgJvvpMPsj1bQD7fVHAwAAAAAAgHJDAdrpuF6Arqlv+WAk/y/HpswK0PWnTk+vmw8jy7IsZ+JPP2zRtCTLtQCtu6SusOnCBej6U61T+dXqwtYWLnQ0q1qwxkd6ouuaaJP4+QrBh0cU3eAyLUDX1DcffhC3Mu/PrUZv/8pwFWdFXLlCLRSg6xtrOx5F8haittCB1q4g7mYpWX3rhctl6/IJBWgAAGCPdwXo775KancmszK+p8im8rXREc3n3vTcMQrQTpMkCtDBpLv8c6H2cyKR8LxARgghhAQxiUTCuAPNItAAAAAAAADuoQDtdEpQgG6sqW/+1c1Zi6Vc0wK07sKxOtKLTz5saaypb6zpiyj+c/kWoOsba078oSOSsl5pNS5AW6hWZ1Ynf3+quUbTgjU70tqPbz82f0HT43e/zK5jbWlw1+J2AbrR+uWTmX58WWTt5yI2Yb7blgrQNfWNu/si2rWuN5bDHc2C+98yobodYnG0p9aVl/vU51HlNK3fuSK4h5UeCtAAAMAezwrQQ9E57b6kB+860FQ+Nqe5vXH95n0K0A6TJArQAVSo/az8rV/2F3+JRGJ1dXVlZcXzAhkhhBASxKysrKyurmZr0MoOdPbHLh1oAAAAAAAAV1GAdjqlKUA31tQ31p76pnt6OSVWoF2aH21uFhi25XL/okHRNzP9+Js9uXVwK6cAXd9YU9+8p/vh8IrFFrRxAbq+sebEH85MrBotSLyRejK8OedWO8q1p65/X3ifM8n58MXNVYErpgCdnZlvwrNJs6WgN1Lxqc8+bbF3LM5eoVYL0DW6HehM/LJgB/rzR7OqmYh1tLjwcmu24tlZF7xQgAYAAPZ4VYB+L5rS7kpy+q+cWKo5v1q9HLtHAdpZkkQBOoDyr3blBZ/7rV+u/by8vOx5gYwQQggJYpaXlzUd6OyPWuWHxbL/vAgAAAAAAOAVCtCBT8Nnx68+7J9ZjCWTq4qma2Y9tbSyOPz00bmvLuxqsDRm854//dg/sxhLrb8YL7OajI8+/vHvT7E4a2NdW2/r3SfD86tLybS6PruRSiWXksmZ2em7Tx+dG+g//unHb50QHfPc4+jESipXls2sp2Lz0f5bvX9p7bXTTctfnv+xfza+vP589PRacmb2SfdXnXVeT6bnqW3602+zr+bm2S5n1lNLS7Hvh78/3maz+qyK81doaXLq9LRqEejV8bDTi0C3dcyqln9+2HfK66MOTChAAwAAezwqQPd3J7R7MjPznQPt597uHb0/XNOUq1PR9yhAO0qSKEAHUKGrPfcrv9w1n20/Ly0teV4gI4QQQoKY7G/lsh3o7CfF3O/mKufzIgAAAAAAgFcoQBNCCNHJR/cnVMtXJwfPO3kLxM+vzigXqM7M33/X80MOTihAAwAAe7wpQF9+8ki7I2vX7zjSfu7e0dvdsqD51pWVL65RgHaSJFGADiDj3/dlL/hEIpFrPy8uLnpeICOEEEKCmMXFxVwHOrsItLIDXSGfFwEAAAAAALxCAZoQQohemg+PrCh/XmTijw6LLWpunhPhoTXl2CvXOlhg3kIoQAMAAHu8KUDfjy1r92PptEPt5x293e9OadaX3rgzQgHaSZJEATpoTJd/zrafc7/yW1xcnJ+f97xARgghhAQx8/PzuQ706upqtgPNItAAAAAAAAClQQGaEEKIfk70DKrKJBuR4a5aB0Y+1TqVVo67ONrjxLAVFArQAADAHk8K0DufxrX7kZh617kC9I4x7fiTU/0UoB0kSRSgg0Zk+efV1dWVlZV4PJ5tP8/NzXleICOEEEKCmLm5uWwHOh6Pr6ys5D4v8hs6AAAAAACAEqAATQghpFBqzz9dVP3QSA6eL3ap5t19kVXlkImnx+++XbQAACAASURBVJxaWLpiQgEaAADY40kB+r1oSrsf8VHH2s+93TuGonPq4Zdj9yhAO0iSKEAHjeY6zy9AJxKJlZWV7O/7FhYWYrHY7Oys5wUyQgghJIiZnZ2NxWILCwvZX8+trKwkEgndArTyU6PXHxYAAAAAAADKBAVoQgghhdN8eHg5o/yxsRY53WJ/wNqOR5ENxWgby+GOYhvVFRgK0AAAwB5PCtAfxtZLfZxFF6wpQCtJEgXooNG9zpW/7MsWoJXLP0ciEc8LZIQQQkgQE4lENItAJxIJ5W/odD81ev1hAQAAAAAAoExQgCaEEGKYsxfmlZ1lORN/dNjems0tgw/XlCNtRIa7ar0/wOCFAjQAALDHkwL0J/GSH2dq5m8oQDtHkihAB43Bb/qUl3p2+ee5ubloNDozM+N5gYwQQggJYmZmZqLR6NzcXG4R6NxHRoNf0nn9YQEAAAAAAKBMUIAmbuRvhlfcO2kf9nl/gJUWXtBKj7a4LK9O39xjtQN9oisc31APMrjb80MLZihAAwAAeyqmAB19jwK0cySJAnRw/KSmW4DWXf55amrK8wIZIYQQEsRMTU3pLgJtXIAu1w+OjvgJAACI8fqHNgAAgC8oPyBRgCZOhb5smYUXlNR23B/XdqCt1JdPdF2IpZVPt7+MNKEADQAA7DL4DQAFaArQhUgSBejg0PwpVPc3fdkCdHb55+ylnv1ln+cFMkIIISSIyf6GLvuRMbsIdLYAbfpLurL84Ciu2MIXAAAw5PWPegAAgNJRfgqiAE2cCn3ZMgsvKKmpb6zteBRRrOC8Gr39K/EGs3oNadrPRYYCNAAAsMfgNwAUoClAFyJJFKCDQ/P3ToPrPFuAnpuby13qnhfICCGEkCAm95Fxbm4uW4AW/NRYlh8cjRXb5AIAALZ4/REAAADAXcpPPhSgiVOhL1tm4QUl2ey++DTbgV6N3bfaYK79+PbjNVmW5Uz86Yct3h9LoEMBGgAA2GPwGwD3CtAfxtY1uzE51V9MO7kEoQCtJEkUoIND8zfO7Am6vr6+vr6+tramuc7n5+fn5uYikcjU1NSzZ888L5ARQgghQcyzZ8+mpqYikcjc3Nz8/LzmU+Pa2lr2B3ElF6CLLW0BAACHeP2hAAAAwBXKDzwUoAkhhBhn98Wnkwsj9tZvru24/2SO9rMDoQANAADsMfgNgHsF6PeiKc1uzEV/8LziTAFanCRRgA4OzZ82Nb/mS6VSiURiZWUlHo9nC9DK3/R5XiAjhBBCghjNL+kWFxfj8fjKykoikcgWoJUfHCuthJTfu/L896qEEEJIpYUaNAAAKHsUoAkhhJBghQI0AACwx5MC9M6ncc1uZJYee15xpgAtTpIoQAeHpQK05jr3vEBGCCGEBDH5nxopQGfl160oQBNCCCGlj+5PZK8/JgAAADiJAjQhhBASrFCABgAA9nhSgN5xP7as2Y/E1LteV5wpQIuTJArQwWH6a75cAXphYYECNCGEEFJ8NJ8aFxYW/n/23i62jfNA2H3vdGWcK18FvsqFLwLjYCHAKLwBAp8ALdwsDmwv0G8RfEa2SHb31EUKo1jDTYMu/AmxgUIw1tqzQlCrlXsau3UCwwk+JXFkxUpky/qxZVuSJUuUJVHWPyVSP5RE0hSZORdjjWaGQ3I4HM5o9D4PnquUHM4Mh5zXLx+91QJoaefpLEMrAmhERERfLHBf9nvIAAAAAOAOBNCIiIjBkgAaAAAAnOFPAH17YtS8H/FPbvtfORNA20QIAujgYDOAXl1dXV5ejkaj6ud8ampqYmLC94AMERExiE5MTExNTamjxmg0ury8vLq6KnMAbdlXZbfwfV4VERFRNrW7MA00AAAA7FYIoBEREYMlATQAAAA4w58AuqWzOWHakezAaIvvlTMBtE2EIIAODqUG0JFIZHZ2lgAaERHRsWoAPTs7G4lECKAL188E0IiIiN6rvxHTQAMAAMCuhAAaERExWBJAAwAAgDN8CqCbf7OQMu1JamXkTb8rZwJomwhBAB0c7ATQ2jSfPoAOh8O+B2SIiIhBNBwO6wNobZ5OwgC6cPqcyWQymYzv86qIiIiyqd6CC2fQfg8iAAAAAMqCABoRETFYEkADAACAM/wKoF9/MDdv3pX1z++5ESvfDbWtrjw1uNz0hADaTYQggA4OBNCIiIgeSwCtUqB+Vrurzc3Nzc1N3+dVERERZVO9BZsyaBpoAAAA2E0QQCMiIgZLAmgAAABwhm8BdMv95oR5HJJYDpW/CPQv55M5Bxn/5DYBtJsIQQAdHJwF0JOTkwTQiIiIzgyHw5OTk5IH0Dbr53Q67fu8KiIiomyqQxEaaAAAANjFEEAjIiIGSwJoAAAAcIZ/AXTzm6MrKfPupDr6W8ra7O1QfyZnoysj5XfVBNB6hCCADg4lTfOpAbQ6zUcAjYiI6Ew1gJ6ZmTEF0PLM0xWun7X0WR2N+D6vioiIKJvqaESfQdNAAwAAwC6DABoRETFYEkADAACAM3wMoC0XgVYy8U/uOd5gy+8Wc5pqJXW3t9z6mQDahBAE0MGBABoREdFjCaDt18+pVMr3eVVERETZTKVS9htov4cVAAAAAE4ggEZERAyWBNAAAADgDF8D6OY3n8ZWcnYpk4r9p5MGuuVXU2uJnK0llkPlL/9MAG1CCALo4FDqNB8BNCIiYpnqA2g55+mK1s8vXrxQ6+dkMun7vCoiIqJsqjNZ6u24aAPt97ACAAAAwAkE0IiIiMGSABoAAACc4W8AnWfNZiWzudb0uLWU7bT/ZzSZtthQOetJE0DnRQgC6OBQzjSf7wEZIiJiEJV8nu4HI4Xr50Qi4fu8KiIiomwmEonCDbTpbu734AIAAACgZAigERERg6XkP6wAAACAY/wOoJtfv917N2U5IMnGVqZr7xfNoFt+Pjg9+CJrtYXM0HibOztJAG1ECALo4EAAjYiI6LEyz9Plq5/1ww91BJJIJDY2NtbX132fV0VERJTN9fX1jY0NNYPWN9DqLZsGGgAAAHYBBNCIiIjBUuYfVgAAAKAc/A+gW5pf/76/w7qBVhTlh1RqvXdh+uMnXf9ybzuG/um9rn95MtK0sDKTtkyfFUXJjDzveNOtPSSANiIEAXRwIIBGRET0WJnn6XIHHvqxhzb80OrntbU13+dVERERZXNtbc3UQKu3af34ZJcNUQAAAEA2CKARERGDpcw/rAAAAEA57IgAuqX59e/77ybzpcwOjmpzyNX6mQDahBAE0MGBABoREdFjZZ6nyzfw0MYe2r8x1Pp5dXXV93lVRERE2VRHJmoDrc5qaeOT3TpEAQAAANkggEZERAyWMv+wAgAAAOWwUwLolubXb3c0Lr1Il39ImY1vB1pd3jcCaCNCEEAHBwJoREREj5V5nq7wwEP9B0YikdDq55WVFd/nVREREWVzZWVFa6DVRaD1DfSuHKIAAACAbBBAIyIiBkuZf1gBAACActhBAXRL8+stzT99/HzwheOloLOxleenv3d/rwigTQhBAB0cCKARERE9Vtp5unyjDv3AI5FIaGOPlZWVpaUl3+dVERERZXNpaUlroDc2NtQGmkWgAQAAYDdBAI2IiBgspf1hBQAAAMpkpwXQr7c0v97Scqzvefd6KmV7nJLJpmeWps93tVRslwigDQhBAB0cCKARERE9Vtp5usKjDu1fF+vr6/F4XK2fo9Go7/OqiIiIshmNRtUGOh6Pr6+va3Nbu3iUAgAAALJBAI2IiBgspf1hBQAAAMpkRwbQW95ue//p2LfRlWeJF/F0Jq3f72wmnk5H1le658dqH939aaX3hADaiBAE0MGBABoREdFjpZ2nMw05cgPoRCKxvr6uDjyWl5djsdji4qLv86qIiIiyubi4GIvFlpeX1SHK+vp6IpGwDKD1AxW/BxoAAAAAJUAAjYiIGCyl/WEFAAAAymRHB9A7SQJoPUIQQAcHAmhERESPlXaeznLIoR91qAG0fvnnSCTi+7wqIiKibEYiEdMi0IlEQj9KsRyo+D3QAAAAACgBAmhERMRgKe0PKwAAAFAmBNAE0A4QggA6OBBAIyIieqy083QFhhz6f1qoyz9Ho9GFhYX5+Xnf51URERFlc35+fmFhIRqNaotAa9NbBQYqfg80AAAAAEqAABoRETFYSvvDCgAAAJQJATQBtAOEIIAODgTQiIiIHivhPN0PRiwDaMvln2dnZ32fV0VERJTN2dlZy0WgCwfQgR6rAAAAgGwQQCMiIgZLCX9YAQAAAFcggCaAdoAQBNDBgQAaERHRYyWcpysaQCeTSTWAVpd/Vv9poY46fJ9XRURElE11lKJOb6mLQKsBdNGBSnDHKgAAACAbBNCIiIjBUsIfVgAAAMAVCKAJoB0gBAF0cCCARkRE9FgJ5+kKB9D6f1eoAXQ0GtX+aeH7vCoiIqJsatNb0WhUDaBtznAFd6wCAAAAskEAjYiIGCwl/GEFAAAAXIEAmgDaAUIQQAcHAmhERESPlXCezjKA3tzc3NzcfPHihenfFUtLS9FoNBKJzM7OTk1N+T6vioiIKJtTU1Ozs7ORSCQajS4tLZlmuF68eKHexAmgAQAAILgQQCMiIgZLCX9YAQAAAFcggCaAdoAQBNDBgQAaERHRYyWcp8sNoPXjjVQqlUgk1tfX4/G4GkDrhxy+z6siIiLKpmmgsrKyEo/H19fXE4mEGkDrxyoE0AAAABBECKARERGDpYQ/rAAAAIArEEATQDtACALo4EAAjYiI6LESztOVFECb/l3h+7wqIiKibObOcBFAAwAAwC6DABoRETFYSvjDCgAAALgCATQBtAOEIIAODgTQiIiIHivhPF3R8YYWQC8vLxNAIyIi+qtphmt5eVkLoHfrWAUAAABkgwAaERExWEr4wwoAAAC4AgE0AbQDhCCADg4E0IiIiB4r4TydzQB6dXV1eXk5Go2q/66YmpqamJjwfV4VERFRNicmJqamptQZrmg0ury8vLq6SgANAAAAuwkCaERExGAp4Q8rAAAA4AoE0ATQDhCCADo4EEAjIiJ6rITzdKUG0JFIZHZ2lgAaERHRF9UAenZ2NhKJEEADAADAroQAGhERMVhK+MMKAAAAuAIBNAG0A4QggA4OBNCIiIgeK+E8nZ0AWhtv6APocDjs+7wqIiKibIbDYX0ArY1VCKABAABg10AAjYiIGCwl/GEFAAAAXIEAmgDaAUIQQAcHAmhERESPlXCejgAaERExQBJAAwAAwK6HABoRETFYSvjDCgAAALgCATQBtAOEIIAODgTQiIiIHivhPJ2zAHpycpIAGhER0XvD4fDk5CQBNAAAAOxiCKARERGDpYQ/rAAAAIArEEATQDtACALo4EAAjYiI6LESztOVNN5QA2h1vEEAjYiI6L1qAD0zM2MKoHfxWAUAAABkgwAaERExWEr4wwoAAAC4AgE0AbQDhCCADg4E0IiIiB4r4TwdATQiImKAJIAGAACAXQ8BNCIiYrCU8IcVAAAAcAUCaAJoBwhBAB0cCKARERE9VsJ5ulLHGwTQiIiIPqoPoCUZqwAAAIBsEEAjIiIGSwl/WAEAAABXIIAmgHaAEATQwYEAGhER0WMlnKfjZ0VERMQAKeFYBQAAAGSDmQpERMRgyWQFAAAAOIMAmgDaAUIQQAcHAmhERESPlXCejp8VERERA6SEYxUAAACQDWYqEBERgyWTFQAAAOAMAmgCaAcIQQAdHAigERERPVbCeTp+VkRERAyQEo5VAAAAQDaYqUBERAyWTFYAAACAMwigCaAdIAQBdHAggEZERPRYCefp+FkRERExQEo4VgEAAADZYKYCERExWDJZAQAAAM4ggCaAdoAQBNDBgQAaERHRYyWcp+NnRURExAAp4VgFAAAAZIOZCkRExGDJZAUAAAA4gwCaANoBQhBABwcCaERERI+VcJ6OnxUREREDpIRjFQAAAJANZioQERGDJZMVAAAA4AwCaAJoBwhBAB0cCKARERE9VsJ5On5WREREDJASjlUAAABANpipQEREDJZMVgAAAIAzCKAJoB0gBAF0cCCARkRE9FgJ5+n4WRERETFASjhWAQAAANlgpgIRETFYMlkBAAAAziCAJoB2gBAE0MGBABoREdFjJZyn42dFRETEACnhWAUAAABkg5kKRETEYMlkBQAAADiDAJoA2gFCEEAHBwJoREREj5Vwno6fFREREQOkhGMVAAAAkA1mKhAREYMlkxUAAADgDAJoAmgHCEEAHRwIoBERET1Wwnk6flZEREQMkBKOVQAAAEA2mKlAREQMlkxWAAAAgDMIoAmgHSAEAXRwIIBGRET0WAnn6fhZERERMUBKOFYBAAAA2WCmAhERMVgyWQEAAADOIIAmgHaAEATQwYEAGhER0WMlnKfjZ0VERMQAKeFYBQAAAGSDmQpERMRgyWQFAAAAOIMAmgDaAUIQQAcHAmhERESPlXCejp8VERERA6SEYxUAAACQDWYqEBERgyWTFQAAAOAMAmgCaAcIQQAdHAigERERPVbCeTp+VkRERAyQEo5VAAAAQDaYqUBERAyWTFYAAACAMwigCaAdIAQBdHAggEZERPRYCefp+FkRERExQEo4VgEAAADZYKYCERExWDJZAQAAAM4ggCaAdoAQBNDBgQAaERHRYyWcp+NnRURExAAp4VgFAAAAZIOZCkRExGDJZAUAAAA4gwCaANoBQhBABwcCaERERI+VcJ6OnxUREREDpIRjFQAAAJANZioQERGDJZMVAAAA4AwCaAJoBwhBAB0cCKARERE9VsJ5On5WREREDJASjlUAAABANpipQEREDJZMVgAAAIAzCKAJoB0gBAF0cCCARkRE9FgJ5+n4WRERETFASjhWAQAAANkodaYiEomoMxXhcNj30RoiIqKEhsNhdbIiEokwWQEAAAD2IYAmgHaAEATQwYEAGhER0WMljIoIoBEREQOkhGMVAAAAkI2SZiqi0SgBNCIior/qA+hoNMpkBQAAANiEAJoA2gFCEEAHBwJoREREj5UwKiKARkREDJASjlUAAABANgigERERgyUBNAAAADiDAJoA2gFCEEAHBwJoREREj5UwKiKARkREDJASjlUAAABANorOVCQSCVMAPTs7SwCNiIjol2oAPTs7awqgE4kEkxUAAABQAAJoAmgHCEEAHRwIoBERET1WwqiIABoREXeGjfWTq896Pjvk5Ll1Z0LxuZGvD/t/FBVXwrEKAAAAyIazAHpqaiocDo+Ojg4PDw8ODvb39z9+/Linp+f+/fudnZ337t1rb2+/c+dOW1vb999//91337W2tt6+ffv27dvfAgAAgA71/tja2vrdd999//33bW1td+7caW9vv3fvXmdn5/3793t6eh4/ftzf3z84ODg8PDw6OhoOh6empgigAQAAoFQIoAmgHSAEAXRwIIBGRET0WAmjIgJoRETcATbWTyYyiqIo6eHOT0psoOvODMXTiqIo2UioyVE/HSQlHKsAAACAbJQZQIdCIS2Afvjw4f3797u6ujo6Otrb2+/evasF0GoDrWXQGn5XZwAAAF5juhWq90f1XqkG0Hfv3m1vb+/o6Ojq6rp///7Dhw+1ADoUChFAAwAAgGMIoAmgHSAEAXRwIIBGRET0WAmjIgJoxKD469C6/sM73O7/LgXP9oidL8bF0Bf+76p3dg/rD36u2499aPz9+FpmeyfSw+2Ntp9bd6Z/eWP7ubu/gZZwrAIAAACyYSeAXl9fX11dXV5e1gfQExMTagD99OnTJ0+e9Pb2Pnz48MGDB2oAfe/evbt37+oXgdYaaMsSGgAAQCr090R9/Xznzp27d+/eu3dPDaAfPHjw8OHD3t7eJ0+ePH36VA2gJyYm9AH08vLy6urq+vo6ATQAAAAUhgCaANoBQhBABwcCaERERI+VMCoigEYMigTQLkgAbaHvAXTdmZC+flaUF9Hrf6mz//S3OyMbhjewpH46eEo4VgEAAADZKDWAVn8BVQPosbGxkZERNYDu6+t79OhRT09Pd3d3Z2entgi0qYHWaAUAAJAY021RXz+ryz93dnZ2d3f39PQ8evSor69PDaBHRkbGxsbUAFrNcQigAQAAwD4E0ATQDhCCADo4EEAjIiJ6rIRREQE0YlAkgHZBAmgLfQ6gj7cb8+XsWtt1+/WzqmkRaGcbCYwSjlUAAABANmwG0PF4fHl52fQLqBpADw0NDQwM9Pf3P378uKen5/79+11dXZ2dnffu3TM10Hq+AwAAkBjTbVFfP9+7d6+zs7Orq+v+/fs9PT2PHz/u7+8fGBgYGhpSA2hTjrO8vByPxwmgAQAAoCgE0ATQDhCCADo4EEAjIiJ6rIRREQE0YlAkgHZBAmgL/QygD10fjWR1r55du/t5vaNN5S4jHblc7/u5rYgSjlUAAABANn4wYpqpSKVSWgCd+wvo+Pj4s2fPhoeHBwcH+/v7e3t7Hz58+ODBg+7u7q6uro6ODq2BVjNotYTW8z0AAIBkmG6F6v1RvVeq9XNHR0dXV1d3d/eDBw8ePnzY29vb398/ODg4PDz87Nmz8fHx3BxHC6BTqZRpssJ0o/d73AEAAAB+QgBNAO0AIQiggwMBNCIiosdKGBURQCMGRQLoCmoMowmgPfL8re6E/rWz0/3Xythg4+W5tH5zmfjomfO+n173lXCsAgAAALJRUgBtmqwIh8Ojo6OhUOjp06dPnjzp6+t7/PixqYE2ZdBaCQ0AAADazVFLn0318+PHj/v6+p48efL06dNQKDQ6OhoOh00zFQTQAAAAYBMCaAJoBwhBAB0cCKARERE9VsKoiAAaMSgSQFdQAmgN7wLoSw2zhl55Y677eJnbPN/W+0K/yWwk1HTI/zPsshKOVQAAAEA2cgPobDa7ubm5ubmpBtD6X0CXlpai0WgkEpmdnZ2ampqYmBgbGxsZGRkaGtIWgX706FFPT4/WQHd2dmoZtEo7AAAAtLe3t7drN0f1XtnZ2anVzz09PY8ePdKWfx4aGhoZGRkbG5uYmJiampqdnY1EItFoVA2g9TnOixcv1Pt47kwFkxUAAACSQwBNAO0AIQiggwMBNCIiosdKGBURQCMGRQLoCkoAreFVAP3WnfkN/eu+mKlzY7XmQ9fHFg13ufW71+v8PsMuK+FYBQAAAGTDMoC2/AV0dXV1eXk5Go3qfwQdHx/XFoEeGBjQGmh1Hej79++rGbRaQqt0AAAAQEdHR0eHdnNU75Xd3d33799X137W6ueBgQFt+efx8XF9ixONRpeXl/UzFYVzHCYrAAAAJIcAmgDaAUIQQAcHAmhERESPlTAqIoBGDIoE0BWUAFrDmwDavFRzsvtLtzLlujMjhk9KJj7ya/9PsptKOFYBAAAA2SgcQKuTFYlEYn19XQ2g1R9BtfmKcDisLgI9PDysb6AfP3788OHDnp4edTXo+/fvqzG0iS4AAADJyL0bqndJddXnnp6ehw8fPn78WF8/Dw8Pq8s/h8NhbZpCbXHUAHp9fT2RSBSdqWCyAgAAQHIIoAmgHSAEAXRwIIBGRET0WAmjIgJoxKBIAF1BCaA1PAmgTRfzxmTbIRe3f/5Wd0K/+Wy4p9Hvk+ymEo5VAAAAQDaKBtCpVEoNoOPx+MrKytLSUjQajUQis7Oz6u+g4XB4dHTU1ED39fWpGbS6GrQWQ6s9NAAAADzYKp7V6Fld9VlNn/v6+kz18+joaDgcVkOc2dnZSCQSjUaXlpZWVlbi8bgaQKdSKQJoAAAAKAABNAG0A4QggA4OBNCIiIgeK2FURACNGBQJoCsoAbSGBwF0fV84q3vFbOx6vcsv8VZ7JKU/qBczded9P8+uKeFYBQAAAOSkcACtn69YXl6ORqPaItBTU1MTExPj4+P6BnpwcPDJkyf9/f36ElrlEQAAAOjQbpFa99zf3//kyZPBwUF9/Tw+Pj4xMTE1NaUt/xyNRtXln/UtToGZCr/HGgAAAOA/OzmA/um9vo9nFnrjiVg6nTD8sJWJp9OR9ZXu+bHanrY3CaA9RwgC6OBAAI2IiOixEkZFBNCIQZEAuoISQGtUPoD+j7Fk5c/2ta+WDHNR0/3X/D7PrinhWAUAAADkxHKyQj9fYbkItPpT6OTkpL6BDoVCQ0NDagY9MDDw5MkTLYZWe2gAAADQ0G6R6h1zYGBATZ+HhoZCoZC+fp6cnFQrHMvln/XTFATQAAAAYMlODKBvd5yfWZpJZ4vvvaIoipLJpkYWxk9/TwDtHUIQQAcHAmhERESPlTAqIoA2W/u3c48mxleTGxn1DGVTqY3pmdE//bW+jM3W//zLh23PY/NJbbOKoiiZzdRqMj4+M9F8p+XntX4f+O455/U///Jh28xKLJlO60/1+spA6OG5Rqfv4/lL/3TjztWBiYH5lZjxfVQy6dVkfPz56F+//OxIWavM1h357E7z2ML0enJtc/sf1ekXydX1ldDY8K15w5q20gXQ5utEyWymYktzbd3fvF3+4r4VCKAPXfzs3KOJ0NLGampTu17USzE0Nvxfn1065MJpceW7xdsAuu7xmH7KaDNyua4iL3SoddbwgUlM/EdFj8tDJRyrAA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)
T1 did some stuff (stuff before) then it called synchronize_rcu() and then it did some other stuff (stuff after). Time flows from top to bottom, i.e. execution order.
The other threads, T2, T3, T4 and T5 are doing rcu_read_lock()/rcu_read_unlock() and we show which events are seen in each situation.
The trickier one here is T5 for which rcu_read_lock() started during the call of synchronize_rcu() (if there was an absolute clock), but because synchronize_rcu() terminated before the call for rcu_read_unlock(), it means that T1 considered that rcu_read_lock() occurred after and so it did have to wait for the call to rcu_read_unlock(), and therefore, T5 will see the stuff that happened before on T1 and may or may not see that stuff that happened after.
Still confused?
That's ok, let's try another way then: The semantics of rcu_read_lock()/rcu_read_unlock() work just like the semantics of a read-lock in a reader-writer lock (only the semantics are similar, the effects are very different). And the semantics for synchronize_rcu(), well, there is no good analogy but you can think of it as having a lock()/unlock() of a writer lock... it's the closest thing I can come up with.
In this algorithm above, each thread calling synchronize_rcu() does a incrementAndGet() on an atomic variable named reclaimersVersion and then spins while waiting for all the ongoing readers to complete by scanning the array of their versions named readersVersion.
Upon calling rcu_read_lock(), a (reader) thread will read the current value in reclaimersVersion and set its uniquely assigned entry in the readersVersion array to the same value, and then re-check that the version has not changed and if so, update the readersVersion with the new entry.
When the read operation is done, it will call rcu_read_unlock() which will set its entry in readersVersion back to NOT_READING, a constant with a special value that means that the reader is not currently active.
It's hard to get any simpler than this ;)
The advantages of this algorithm are:
- Very light in synchronization on the reader's side: Only two sequentially consistent loads and one seq-cst store on rcu_read_lock() and a release store in rcu_read_unlock();
- Wait-free for readers: No loops or retries on either rcu_read_lock() or rcu_read_unlock();
- Starvation-free for reclaimers: Calls to synchronize_rcu() are free from starvation... non-obvious why, but true;
- Multiple reclaimers can make progress simultaneously: Unlike some of the other Userspace RCU implementations that have a mutual exclusion lock inside synchronize_rcu(), this one does not, which means it scales well for multiple reclaimers;
- Code is short and sweet: There can't be many hidden bugs in something that takes less than 20 lines of code;
The only true subtle detail about this algorithm is in the fact that rcu_read_lock() does a load, a store, a load and then a relaxed store. The first load and store would suffice for the algorithm itself, but the thing about volatile/seq-cst stores is that they allow regular code to be reordered from below to above. This means that we could have code being executed before the readersVersion.set(tid, rv) which would be incorrect from the point of view of the expected semantics of RCU, and therefore, we need some kind of barrier to prevent code from traveling up. The easiest way to accomplish this is to do a volatile/seq-cst load, and since we're doing that, we might as well check if it changed and if it did, then we might as well update it with a lazy (or relaxed) store.
Like I said, it's subtle and not too important to understand how the algorithm works, it's more of an implementation detail.
Why would anyone want use RCU in Java? RCU is for memory reclamation, and Java has a GC, right?
Like I mentioned before, RCU is not (just) about memory reclamation, it's about using quiescence in your application to do whatever you want with quiescence, and as amazing as it may seem, there is a non-negligible amount of things you can do with it!
For example, suppose you have a pool of complex objects that are too costly to create, so you want to re-use by returning to the pool. But this pool is concurrent, so you have to make sure that some thread that is lagging behind isn't holding a reference to the object anymore.
After returning the object to the pool, when is it safe to re-use the object?
The answer is RCU: Protect all accesses to the objects from that pool with an rcu_read_lock()/rcu_read_unlock() and only reuse from the pool after calling synchronize_rcu().
Another example is Relativistic Programming which you can see more of in this paper:
http://web.cecs.pdx.edu/~walpole/papers/haskell2015.pdf
A funny thing about RCU is that one single RCU instance can be used for as many purposes as desired, for example, one RCU instance for all the pools (if there are more than one), or one per pool, as you prefer.
We'll prepare a C++ implementation for a future post.
So, what is RCU again?
Yeah, well, RCU is many things and it has been talked about it detail by one of its original authors here.
... no, I mean, like, what is RCU, like _really_?
In its most basic form, RCU is a concurrency construct to provide on-demand quiescence.
By concurrency construct I mean it's a building block to use in a concurrency setting, the same way that a mutual exclusion lock is a concurrency construct, or a semaphore, or a reader-writer lock, etc. And by on-demand quiescence I mean that you can introduce quiescence anywhere in your application, as needed (more on that later).
RCU's basic API is:
- rcu_read_lock()
- rcu_read_unlock()
- synchronize_rcu()
with the first two being called in a block, i.e. for every rcu_read_lock() there must be a later rcu_read_unlock(). Also, we can not call synchronize_rcu() from within a block of code enclosed by rcu_read_lock() / rcu_read_unlock().
Now, unlike the examples I gave of locks and semaphores, RCU does not provide any kind of mutual exclusivity property, at least not directly, but it does provides two very interesting guarantees:
First, the effects of any operation that happens before synchronize_rcu() in one thread (T1), will for sure be visible in another thread (T2) after rcu_read_lock(), if the call to synchronize_rcu() in T1 returned before the call to rcu_read_lock() in T2;
Second, the effects of any operation that happens after synchronize_rcu() in one thread (T1), will for sure not be visible in another thread (T2) before rcu_read_unlock(), if the call to rcu_read_lock() in T2 started before the call to synchronize_rcu() in T1.
... yes, it's confusing when you read it for the first time, but the idea is pretty simple. Here is a schematic that may help to understand:
T1 did some stuff (stuff before) then it called synchronize_rcu() and then it did some other stuff (stuff after). Time flows from top to bottom, i.e. execution order.
The other threads, T2, T3, T4 and T5 are doing rcu_read_lock()/rcu_read_unlock() and we show which events are seen in each situation.
The trickier one here is T5 for which rcu_read_lock() started during the call of synchronize_rcu() (if there was an absolute clock), but because synchronize_rcu() terminated before the call for rcu_read_unlock(), it means that T1 considered that rcu_read_lock() occurred after and so it did have to wait for the call to rcu_read_unlock(), and therefore, T5 will see the stuff that happened before on T1 and may or may not see that stuff that happened after.
Still confused?
That's ok, let's try another way then: The semantics of rcu_read_lock()/rcu_read_unlock() work just like the semantics of a read-lock in a reader-writer lock (only the semantics are similar, the effects are very different). And the semantics for synchronize_rcu(), well, there is no good analogy but you can think of it as having a lock()/unlock() of a writer lock... it's the closest thing I can come up with.
What does an RCU implementation looks like in Java?
static class RCU {
final static long NOT_READING = Long.MAX_VALUE;
final static int MAX_THREADS = 128;
final AtomicLong reclaimerVersion = new AtomicLong(0);
final AtomicLongArray
readersVersion = new
AtomicLongArray(MAX_THREADS);
public RCU() {
for (int i=0; i < MAX_THREADS; i++) readersVersion.set(i, NOT_READING);
}
public static int getTID() {
return (int)(Thread.currentThread().getId()
% MAX_THREADS);
}
public void read_lock(final int tid) { // rcu_read_lock()
final long rv = reclaimerVersion.get();
readersVersion.set(tid, rv);
final long nrv = reclaimerVersion.get();
if (rv != nrv) readersVersion.lazySet(tid, nrv);
}
public void read_unlock(final int tid) { //
rcu_read_unlock()
readersVersion.set(tid, NOT_READING);
}
public void
synchronize_rcu() {
final long waitForVersion = reclaimerVersion.incrementAndGet();
for (int i=0; i < MAX_THREADS; i++) {
while (readersVersion.get(i) < waitForVersion) { } // spin
}
}
}
In this algorithm above, each thread calling synchronize_rcu() does a incrementAndGet() on an atomic variable named reclaimersVersion and then spins while waiting for all the ongoing readers to complete by scanning the array of their versions named readersVersion.
Upon calling rcu_read_lock(), a (reader) thread will read the current value in reclaimersVersion and set its uniquely assigned entry in the readersVersion array to the same value, and then re-check that the version has not changed and if so, update the readersVersion with the new entry.
When the read operation is done, it will call rcu_read_unlock() which will set its entry in readersVersion back to NOT_READING, a constant with a special value that means that the reader is not currently active.
It's hard to get any simpler than this ;)
The advantages of this algorithm are:
- Very light in synchronization on the reader's side: Only two sequentially consistent loads and one seq-cst store on rcu_read_lock() and a release store in rcu_read_unlock();
- Wait-free for readers: No loops or retries on either rcu_read_lock() or rcu_read_unlock();
- Starvation-free for reclaimers: Calls to synchronize_rcu() are free from starvation... non-obvious why, but true;
- Multiple reclaimers can make progress simultaneously: Unlike some of the other Userspace RCU implementations that have a mutual exclusion lock inside synchronize_rcu(), this one does not, which means it scales well for multiple reclaimers;
- Code is short and sweet: There can't be many hidden bugs in something that takes less than 20 lines of code;
The only true subtle detail about this algorithm is in the fact that rcu_read_lock() does a load, a store, a load and then a relaxed store. The first load and store would suffice for the algorithm itself, but the thing about volatile/seq-cst stores is that they allow regular code to be reordered from below to above. This means that we could have code being executed before the readersVersion.set(tid, rv) which would be incorrect from the point of view of the expected semantics of RCU, and therefore, we need some kind of barrier to prevent code from traveling up. The easiest way to accomplish this is to do a volatile/seq-cst load, and since we're doing that, we might as well check if it changed and if it did, then we might as well update it with a lazy (or relaxed) store.
Like I said, it's subtle and not too important to understand how the algorithm works, it's more of an implementation detail.
Why would anyone want use RCU in Java? RCU is for memory reclamation, and Java has a GC, right?
Like I mentioned before, RCU is not (just) about memory reclamation, it's about using quiescence in your application to do whatever you want with quiescence, and as amazing as it may seem, there is a non-negligible amount of things you can do with it!
For example, suppose you have a pool of complex objects that are too costly to create, so you want to re-use by returning to the pool. But this pool is concurrent, so you have to make sure that some thread that is lagging behind isn't holding a reference to the object anymore.
After returning the object to the pool, when is it safe to re-use the object?
The answer is RCU: Protect all accesses to the objects from that pool with an rcu_read_lock()/rcu_read_unlock() and only reuse from the pool after calling synchronize_rcu().
Another example is Relativistic Programming which you can see more of in this paper:
http://web.cecs.pdx.edu/~walpole/papers/haskell2015.pdf
A funny thing about RCU is that one single RCU instance can be used for as many purposes as desired, for example, one RCU instance for all the pools (if there are more than one), or one per pool, as you prefer.
We'll prepare a C++ implementation for a future post.
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