- Haiku monthly activity report - 08/2015
- 'Packaging Infrastructure' Final Weekly Report
- Haiku monthly activity report - 07/2015
- 'Packaging Infrastructure' Contract Weekly Report #7
- Media Kit: New And Old Pieces
- 'Packaging Infrastructure' Contract Report #6
- Debugger: Step 1: Edit variable, Step 2: ..., Step 3: Profit!
- Back from RMLL/LSM!
- 'Packaging Infrastructure' Contract Report #5
- Introducing the launch_daemon
New scheduler merged
As you undoubtedly know, my scheduler branch has been merged a month ago. Also, some important changes has been made since, including bug fixes and performance improvements. It is now time to sum up what already has been done, and show some long promised benchmark results. There are still some issues that need to be addressed, but I do not think that any of them is a major one.
Most of the interesting work had already been done when I wrote my last report. The rest of my contract time was mostly spent tweaking, fixing and improving. However, in order to make this task easier I implemented very simple profiler which, when enabled, collects data regarding time taken to execute each function. Not wanting to spent to much writing this profiler, which is merely a tool, I have chosen the simplest approach that requires each function the profiler is to recognize to include macro
SCHEDULER_ENTER_FUNCTION(). Additional and very important advantage of this solution is that the profiler is aware of inlined functions as well what would not be the case if the profiler relied solely on the information available at run time. As a result I got a very useful tool that is able to show me statistics: number of calls, total inclusive time, total exclusive time, average inclusive time per call and average exclusive time per call.
Using the information obtained by the profiler I was able to identify and deal with many unnecessary bottlenecks in the scheduler implementation. An example of one would be the effective priority of a thread. As I explained in my previous posts thread may earn a penalty that temporarily reduces its priority. Since the effective priority of a thread is needed by various parts of the scheduler logic and it was computed each time it was needed a lot of time was wasted. The solution was easy: remember the effective priority and recompute it only when the penalty or the basic priority are changed.
The profiler also showed that quite a large amount of time is spent determining which thread should run next. Improving this required changes both in run queue implementation and locking of scheduler internal structures. Originally, run queues used a bitmap to quickly determine what is the highest priority of enqueued threads. That made enqueueing very fast as it required only to set appropriate bit in a bitmap, but in order to dequeue a thread the most significant bit had to be found what is an O(n) operation, n being the number of possible thread priorities (note that the data structure is still O(1) as the number of possible thread priorities is bounded). I decided to use heap instead to store the information on the highest priority. This means that both enqueue and dequeue are now O(log n), n being the number of possible thread priorities (the run queue is still O(1)). Testing this change with the profiler confirmed that decrease in dequeue time is worth the slight increase in enqueue time as attempts to dequeue a thread are more often than enqueue operations.
Another change made to reduce time needed to chose next thread was using separate lock to protect per CPU run queues. Generally, every ready thread is stored in a run queue owned by the core it is assigned to. However, to allow temporarily pinning threads to a certain logical processor each CPU also has its own run queue that contains all ready threads that are pinned to that particular logical processor. For the sake of simplicity, run queue of a core and run queues of all logical processors that belong to that core were protected by a single lock. However, despite the changes I described in previous paragraph, operations on run queue are quite costly (compared to e.g. a simple FIFO) and there is no reason why each of these queues can not be protected by its own lock.
There was also a problem with thread migration when all cores became overloaded. Since the core load was computed as a percentage of time spent doing actual work its maximum value was 100%. In a 2 core system with 4 CPU bound threads the scheduler was not able to distinguish between a situation when 1 of the threads is on the first core and 3 on the second and a situation when both cores have two threads assigned, in each case core load would be 100%. To mitigate this problem I made quite serious changes in the way core load is computed. Firstly, instead of computing the load each thread produces the scheduler attempts to estimate how much load would that thread produce if there was no other thread in the system. Then, core load is computed by summing such load estimation of every thread that has been running on the core during previous measurement interval and is likely to return (i.e. still exists and has not been migrated). This solution allows load balancing to work properly even if all cores are overloaded.
The logic regarding thread management on a core also needed some tweaks. I was not satisfied by the performance of the scheduler in terms of responsiveness, during some more challenging scenarios and adding more heuristics did not seem to be a good idea. Instead, I made the effective priority of every thread continuously cycle between its basic priority minus penalty and the lowest possible. This enabled me to simplify the code that decides whether priority penalty should be increased or cancelled as priority penalties as no longer the main way to prevent thread starvation. In addition to that I made the limitation of thread time slice length more strict. The idea, which I described in my previous posts, was to define a maximum latency between a thread becoming ready and starting to execute. Then when a time slice length of a thread was computed that maximum latency value was divided by the number of ready threads. This was never meant to be very precise or strict (nor it is now), however, I attempted to make the enforcement of maximum latency more effective. To achieve that I took a different approach to dealing with thread time slices. Now, instead of keeping track of time left the scheduler stores information how much of its time slice thread has already used and, each time it is about to run, its time slice is recomputed using current number of ready threads in the run queue thus trying to dynamically adjust time slice length to the current situation.
These are the most important changes I made since my last post in December. My goal was to introduce a modern scheduler algorithm that is aware of the hardware properties including cache, power usage, SMT and to make the whole kernel perform better on multiprocessor systems by either making locks more fine grained or trying to avoid them at all. There is still room for improvement both in the scheduler and the whole kernel, though, the benchmark results show that there is a decrease of performance in some scenarios. Identifying some of these problems may be more challenging because of the significant changes in locking in the kernel which shifted lock contention to other places that were not bottlenecks earlier.
All test were done using UnixBench 5.1.3 on gcc2h build with test programs built with gcc2. More is better in all tests. The last column is the difference between hrev46689 (the last revision before the new scheduler was merged) and hrev46861. The scheduler was set to low latency mode. Test on Intel i7 4770 were run with both cpuidle and cpufreq enabled.
Intel Core 2 Duo 1 thread: hrev46689 hrev46861 Dhrystone 2 using register variables 7115136.8 lps 7280919.5 lps 2.3% Double-Precision Whetstone 2053.7 MWIPS 2053.3 MWIPS 0.0% Execl Throughput 944.6 lps 1089.3 lps 15.3% File Copy 1024 bufsize 2000 maxblocks 297053.1 KBps 333946.7 KBps 12.4% File Copy 256 bufsize 500 maxblocks 77672.1 KBps 88647.2 KBps 14.1% File Copy 4096 bufsize 8000 maxblocks 716887.5 KBps 788334.1 KBps 10.0% Pipe Throughput 725684.6 lps 860490.1 lps 18.6% Pipe-based Context Switching 170368.4 lps 128296.0 lps -24.7% Shell Scripts (1 concurrent) 637.5 lpm 573.9 lpm -10.0% Shell Scripts (8 concurrent) 119.3 lpm 81.6 lpm -31.6% System Call Overhead 775816.8 lps 960850.4 lps 23.9% 2 threads: hrev46689 hrev46861 Dhrystone 2 using register variables 13984823.4 lps 14114026.6 lps 0.9% Double-Precision Whetstone 4092.9 MWIPS 4099.5 MWIPS 0.2% Execl Throughput 1706.7 lps 1941.1 lps 13.7% File Copy 1024 bufsize 2000 maxblocks 446325.4 KBps 457893.4 KBps 2.6% File Copy 256 bufsize 500 maxblocks 117417.7 KBps 119450.6 KBps 1.7% File Copy 4096 bufsize 8000 maxblocks 145707.2 KBps 1076602.3 KBps 638.9% Pipe Throughput 1449905.9 lps 1711562.9 lps 18.0% Pipe-based Context Switching 226215.1 lps 190808.2 lps -15.7% Shell Scripts (1 concurrent) 908.3 lpm 685.8 lpm -24.5% Shell Scripts (8 concurrent) 117.7 lpm 75.4 lpm -35.9% System Call Overhead 1490019.4 lps 1830241.4 lps 22.8%
Quite predictable. First two tests do not show much difference since they depend mostly on the CPU performance. "File copy" tests are the one that use cache most hence the significant increase in performance in one of them. Intel Core 2 Duo has enough L1 cache to fit most of the data used by both threads during the tests and, obviously, nothing else was running during that benchmark, as a result the benefits of cache affinity are not fully shown here. Also, "Pipe-based Context Switching" and "Shell Scripts" show decrease in performance which, despite being a bad thing, is not very surprising. The complexity of the scheduler has increased and it has problems with short lived threads (bug #10454).
Intel i7 4770 1 thread: hrev46689 hrev46861 Dhrystone 2 using register variables 11982727.0 lps 12005758.1 lps 0.2% Double-Precision Whetstone 2514.3 MWIPS 2515.7 MWIPS 0.1% Execl Throughput 1587.8 lps 1794.9 lps 13.0% File Copy 1024 bufsize 2000 maxblocks 538815.6 KBps 593178.5 KBps 10.1% File Copy 256 bufsize 500 maxblocks 137242.3 KBps 155385.4 KBps 13.2% File Copy 4096 bufsize 8000 maxblocks 1615828.2 KBps 1584127.7 KBps -2.0% Pipe Throughput 1422386.5 lps 1608623.9 lps 13.1% Pipe-based Context Switching 227089.9 lps 179972.5 lps -20.7% Shell Scripts (1 concurrent) 881.5 lpm 1026.3 lpm 16.4% Shell Scripts (8 concurrent) 267.6 lpm 265.9 lpm -0.6% System Call Overhead 1465359.6 lps 1670277.3 lps 14.0% 4 threads: hrev46689 hrev46861 Dhrystone 2 using register variables 41485947.8 lps 47924921.0 lps 15.5% Double-Precision Whetstone 9639.3 MWIPS 10061.3 MWIPS 4.4% Execl Throughput 1572.6 lps 1920.5 lps 22.1% File Copy 1024 bufsize 2000 maxblocks 171534.6 KBps 73747.3 KBps -57.0% File Copy 256 bufsize 500 maxblocks 46656.0 KBps 49328.8 KBps 5.7% File Copy 4096 bufsize 8000 maxblocks 182597.6 KBps 294309.9 KBps 61.2% Pipe Throughput 4943965.2 lps 6385496.1 lps 29.2% Pipe-based Context Switching 235802.3 lps 529553.1 lps 124.6% Shell Scripts (1 concurrent) 2180.3 lpm 2087.7 lpm -4.2% Shell Scripts (8 concurrent) 280.2 lpm 255.6 lpm -8.8% System Call Overhead 4262084.0 lps 5017380.6 lps 17.7% 8 threads: hrev46689 hrev46861 Dhrystone 2 using register variables 53714831.3 lps 54277995.3 lps 1.0% Double-Precision Whetstone 17506.3 MWIPS 17812.0 MWIPS 1.7% Execl Throughput 1606.1 lps 1747.1 lps 8.8% File Copy 1024 bufsize 2000 maxblocks 74140.5 KBps 57900.0 KBps -21.9% File Copy 256 bufsize 500 maxblocks 43026.5 KBps 46933.9 KBps 9.1% File Copy 4096 bufsize 8000 maxblocks 173899.3 KBps 207896.0 KBps 19.5% Pipe Throughput 7285875.6 lps 8710492.6 lps 19.6% Pipe-based Context Switching 300171.7 lps 864860.7 lps 188.1% Shell Scripts (1 concurrent) 2150.6 lpm 1986.6 lpm -7.6% Shell Scripts (8 concurrent) 267.3 lpm 230.8 lpm -13.7% System Call Overhead 6429142.5 lps 6818879.6 lps 6.1%
Results of "File Copy" tests are surprising. Unfortunately, these tests involves almost all kernel subsystems (vm, vfs, block drivers) and BFS implementation what will make tracking this problem a bit more complicated. Since this performance drop is not present on all machines it is possible that it is not directly caused by the scheduler but rather an suboptimal implementation somewhere else. That needs to be investigated though. As for other tests, running four threads is the only situation that shows noticeable performance increase in the first two more CPU bound tests. That is what one would expect since Intel i7 4770 implements SMT and the new scheduler is aware of the problems it may create. There is also very significant increase in performance of "Pipe-based Context Switching". The reason for that is the fact that the old scheduler did not scale at all since it was protected by big
gSchedulerLock (you can actually see that in the results of this test as they do not change much no matter whether one or eight threads are involved). This is no longer true, the scheduler uses much more fine grained locking.
Definitely, there are still many things that need to be taken care of. Some tunables could be set better, starvation prevention could be more effective what would allow time slices to be longer, thread migration could make better decisions. There is also the rest of the kernel which could avoid extensive lock contention more. However, the scheduler and the other changes were necessary and inevitable since sooner or later we would have to start caring about cache affinity, SMT, power usage and lock contention. I hope that what already has been done will make further improvements in that area much easier to achieve.