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The Performance Impact of Kernel Prefetching on Buffer Cache Replacement Algorithms

The Performance Impact of Kernel Prefetching on Buffer Cache Replacement Algorithms. ( ACM SIGMETRIC ’05 ) ACM International Conference on Measurement & Modeling of Computer Systems Ali R. Butt, Chris Gniady, Y. Charlie Hu Purdue University. Presented by Hsu Hao Chen. Outline. Introduction

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The Performance Impact of Kernel Prefetching on Buffer Cache Replacement Algorithms

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  1. The Performance Impact of Kernel Prefetching on BufferCache Replacement Algorithms (ACM SIGMETRIC ’05) ACM International Conference on Measurement & Modeling of Computer Systems Ali R. Butt, Chris Gniady, Y. Charlie Hu Purdue University Presented by Hsu Hao Chen

  2. Outline • Introduction • Motivation • Replacement Algorithm • OPT • LRU • LRU-2 • 2Q • LIRS • LRFU • MQ • ARC • Performance Evaluation • Conclusion

  3. Introduction • Improving file system performance • Design effective block replacement algorithms for the buffer cache • Almost all buffer cache replacement algorithms have been proposed and studied comparatively without taking into account file system prefetching which exists in all modern operating systems • Cache hit ratio is used as sole performance metric • The actual number of disk I/O requests? • The actual running time of applications?

  4. Introduction (Cont.) • Kernel Prefetching in Linux • Beneficial for sequential accesses Various kernel components on the path from file system operation to the disk

  5. Motivation • The goal of buffer replacement algorithm • Minimize the number of disk I/O • Reduce the running time of the applications • Example Without prefetching, Belady results in 16 misses LRU results in 23 misses With prefetching, Beladys is not optimal!

  6. Replacement Algorithm • OPT • Evicts the block that will be referenced farthest in the future • Often used for comparative studies • Prefetched blocks are assumed to be accessed most recently, OPT can immediately determine wrong or right prefetches

  7. Replacement Algorithm • LRU • Replaces the page that has not been accessed for the longest time • Prefetched blocks are inserted in the MRU just like regular blocks

  8. Replacement Algorithm • LRU pathological case • the working set size is larger than the cache • The application has a looping access pattern • In this case, LRU will replace all blocks before they are used again

  9. Replacement Algorithm • LRU-2 • Try to avoid the pathological cases of LRU • LRU-K replaces a block based on the Kth-to-the-last reference • Authors recommended K=2 • LRU-2 can quickly remove cold blocks from the cache • Each block access requires log(N) operations to manipulate a priority queue N is the number of blocks in the cache

  10. Replacement Algorithm • 2Q • Proposed • Achieve similar page replacement performance to LRU-2 • Low overehad way (constant LRU) • All missed blocks in A1in queue • Address of replaced blocks in A1out queue • Re-referenced blocks in Am queue • Prefetched blocks are treated as on-demand blocks and if prefetched block is evicted from A1in queue before on-demand access, it is simply discarded

  11. Replacement Algorithm • 2Q

  12. Replacement Algorithm • LIRS (Low Inter-reference Recency Set) • LIR block : if accessed again since inserted on the LRU stack HIR block : referenced less frequently • Insert prefetched blocks into the cache that maintains HIR blocks

  13. Replacement Algorithm • LRFU (Least Recently/Frequently Used) • Replaces the block with the smallest C(x) value • Prefetched blocks are treated as the most recently accessed • Problem: how to assign the initial weight (c(x)) • Solution: a prefetched flag is set • When the block is accessed on-demand • Initial value every block x，at every time t，λ a tunable parameter Initially，assign a value C(x)=0

  14. Replacement Algorithm • MQ (Multi-Queue) • Use m LRU queues (typically m=8) • Q0,Q1,….Qm-1，where Qi contains blocks that have been at least 2i times but no more than 2i+1-1 times recently • Not increments the reference counter when a block is prefetched

  15. Replacement Algorithm • MQ (Multi-Queue)

  16. Replacement Algorithm • ARC (Adaptive Replacement Cache) • Maintains two LRU lists • Pages that have been referenced only once (L1) • Pages that have been referenced at least twice (L2) • Each list has same length c as cache • Cache contains tops of both lists: T1 and T2 L-1 L-2 |T1| + |T2| = c T1 T2

  17. Replacement Algorithm • ARC attempts to maintain a Buffer size B_T1 for list T1 • When cache is full, ARC replacement • if |T1| > B_T1 LRU page from T1 • otherwise LRU page from T2 • if prefetched block is already in the ghost queue, it is not moved to the second queue, but to the first queue

  18. Performance Evaluation • Simulation Environment • implement a buffer cache simulator • functionally (prefetching, I/O clustering) Linux • With DiskSim, they simulate the I/O time of applications Application Sequentialaccess Randomaccess Multi1 : workload in a code development environment Multi2 : workload in a graphic development and simulation Multi2 : workload in a database and a web index server

  19. Performance Evaluation (Cont.) cscope (sequential) Hit ratio # of clustered disk requests Execution time

  20. Performance Evaluation (Cont.) cscope (sequential) Hit ratio # of clustered disk requests Execution time

  21. Performance Evaluation (Cont.) glimpse (sequential) Hit ratio # of clustered disk requests Execution time

  22. Performance Evaluation (Cont.) tph-h (random) Hit ratio # of clustered disk requests Execution time

  23. Performance Evaluation (Cont.) tph-r (random) Hit ratio # of clustered disk requests Execution time

  24. Performance Evaluation (Cont.) • Concurrent applications • Multi1 : hit ratios and disk requests with or without prefetching exhibit similar behavior as cscope • Multi2 : behavior is similar to multi1, but prefetching does not improve the execution time (CPU-bound viewperf) • Multi3 : behavior is similar to tpc-h • Synchronous vs. asynchronous prefetching With prefetching, number of requests is at least 30% lower than without prefetching except OPT, especially whenasynchronous prefetching is used Number and size of disk I/O (cscope at 128MB cache size)

  25. Conclusion • Kernel prefetching performance can have significant impact • different replacement algorithms • Application file access patterns importance for prefetching disk data • Sequential access • Random access • With prefetching or without prefetching, hit ratio is not sole performance metric

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