How s the Parallel Computing Revolution Going? Towards Parallel, Scalable VM Services
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1 How s the Parallel Computing Revolution Going? Towards Parallel, Scalable VM Services Kathryn S McKinley The University of Texas at Austin Kathryn McKinley Towards Parallel, Scalable VM Services 1
2 20 th Century Simplistic Hardware View Faster Processors Frequency Scaling Speculation, OO programs do not change they just run faster Kathryn McKinley Towards Parallel, Scalable VM Services 2
3 Programming Language Evolution Managed Programming Languages Native Programming Languages 3
4 20 th Century Simplistic Software View Larger, More Capable Software Managed Languages hardware does not change it just runs faster Kathryn McKinley Towards Parallel, Scalable VM Services 4
5 Processor Technology Evolution Power 5 2 cores (90nm) 2004 Core 2 Duo Conroe (65nm) 2006 Pentium M Dothan (90nm) 2005 Pentium 4 NetBurst (130nm) 2003 Core 2 Duo Wolfdale (45nm) 2009 i5 Clarkdale (32nm) 2010 i7 Bloomfield (45nm) 2008 Atom Diamondville (45nm)
6 The 20 th Century Virtuous Cycle Faster Single Processor Frequency Scaling Larger, More Capable Software Managed Languages Kathryn McKinley Towards Parallel, Scalable VM Services 6
7 The 21 st Century Virtuous Cycle? More Cores Chip Multiproccesors CMP? Scalable Software Scalable Apps + Scalable Runtime Kathryn McKinley Towards Parallel, Scalable VM Services 7
8 How is this new virtuous cycle going? Kathryn McKinley Towards Parallel, Scalable VM Services 8
9 Measured Power vs Performance Power (W) (log) Pentium 4 (130nm) Core 2 Duo (65nm) i7 (45nm) ?? Core 2 Duo (45nm) i5 (32nm) Speedup (v Atom 230) (log) SPEC CPU 2006, DaCapo, SPEC jvm98
10 How is this new virtuous cycle going for single threaded Java Kathryn McKinley Towards Parallel, Scalable VM Services 10
11 Performance Scaling Single Threaded Java Benchmarks Core i7: 4 cores, 2 way SMT Speedup Hardware Contexts antlr bloat compress db fop jack javac jess mpegaudio pmd raytrace geomean Kathryn McKinley Towards Parallel, Scalable VM Services 11
12 How is this new virtuous cycle going for multi-threaded Java Kathryn McKinley Towards Parallel, Scalable VM Services 12
13 Performance Scaling Multi-Threaded Java Benchmarks Core i7: 4 cores, 2 way SMT 4.0 Speedup Hardware Contexts avrora batik eclipse h2 jython luindex lusearch mtrt pjbb2005 sunflow tomcat tradebeans tradesoap xalan geomean Kathryn McKinley Towards Parallel, Scalable VM Services 13
14 Power, Performance, and Concurrency Native Java Single threaded hollow; multithreaded solid Microarchitecture changes from Pentium 4 (130) to i5 (32) favored parallelism-no surprise Multithreaded performance incurs a significant power cost 14
15 Is there hope? Kathryn McKinley Towards Parallel, Scalable VM Services 15
16 Managed Languages Challenges & Opportunities Kathryn McKinley Towards Parallel, Scalable VM Services 16
17 Must Start with a Scalable Managed Runtime Kathryn McKinley Towards Parallel, Scalable VM Services 17
18 Sequential Managed Programs time Single Core Application Managed Runtime Profiling Dynamic Analysis Compilation Garbage Collection Other Helper Threads Kathryn McKinley Towards Parallel, Scalable VM Services 18
19 Steps towards scalability Step 1. Parallel application time Core 0 Core 1 Core 2 Core 3 Core 4 Core 5 Application Threads Core 6 Core 7 Unused cores Each thread has different running time Kathryn McKinley Towards Parallel, Scalable VM Services 19
20 Steps towards scalability Step 2. Parallel runtime time Core 0 Core 1 Core 2 Application Managed Application Core 3 Threads Runtime Threads Core 4 Core 5 Core 6 Core 7 Runtime waits for all application threads to pause Kathryn McKinley Towards Parallel, Scalable VM Services 20
21 Steps towards scalability Step 3. Parallel & concurrent runtime time Core 0 Core 1 Core 2 Application Managed Application Core 3 Threads Runtime Threads Core 4 Core 5 Core 6 Core 7 Managed runtime on application s critical path may perturb its performance Kathryn McKinley Towards Parallel, Scalable VM Services 21
22 Steps towards scalability Ideal model Step 4. Minimize perturbation time Core 0 Core 1 Core 2 Core 3 Core 4 Application Threads Application Threads Core 5 Core 6 Core 7 Whole runtime task taken off critical path Application offloads work to concurrent runtime threads Kathryn McKinley Towards Parallel, Scalable VM Services 22
23 Steps towards scalability Ideal model Step 4. Minimize perturbation time Core 0 Core 1 Core 2 Core 3 Core 4 Core 5 Application Threads Threads Application Core 6 Core 7 Worst case is parallel & concurrent Kathryn McKinley Towards Parallel, Scalable VM Services 23
24 Vision Scalable Runtimes Runtime & application parallelism & concurrency CMP aware runtime improves application scalability Communication Cache coherency is expensive and performance sensitive Memory bandwidth scaling is problematic Heterogeneity Move non-critical path off power-hungry cores Smarter, more aggressive analysis Specialization? Tuned cores? Special purpose cores? Kathryn McKinley Towards Parallel, Scalable VM Services 24
25 Approach Profiling (feedback directed optimization) Concurrent analysis More invasive analysis on low-power cores GC High performance concurrent GC High performance non-moving GC Reduced synchronization overheads Distributed & scratchpad GC JIT Concurrent, parallel JIT Cost-benefit shift as low-power cores used Architecture Tuned and/or specialized cores for runtime services Coherence tailed for restricted, common case of GC Kathryn McKinley Towards Parallel, Scalable VM Services 25
26 Today Profiling (feedback directed optimization) Concurrent analysis More invasive analysis on low-power cores GC High performance concurrent GC High performance non-moving GC Reduced synchronization overheads Distributed & scratchpad GC JIT Concurrent, parallel JIT Cost-benefit shift as low-power cores used Architecture Tuned and/or specialized cores for runtime services Coherence tailed for restricted, common case of GC Kathryn McKinley Towards Parallel, Scalable VM Services 26
27 A Concurrent Dynamic Analysis Framework For CMP Hardware Jungwoo Ha U. Texas & UCS/ICI-East Matthew Arnold IBM Research Stephen M. Blackburn Australian National University Kathryn S. McKinley University of Texas at Austin Kathryn McKinley Towards Parallel, Scalable VM Services 27
28 Generic Sequential Analysis instrumented code (== overhead) time Application data collection analysis Difficult to optimize instrumented code Trade accuracy for overhead (sampling) Kathryn McKinley Towards Parallel, Scalable VM Services 28
29 Generic Concurrent Analysis instrumented code (reduced overhead) time Application data collection dequeue enqueue buffering analysis Application (producer) Analysis (consumer) Lower overhead & higher accuracy Must deal with microarchitectural side-effects Kathryn McKinley Towards Parallel, Scalable VM Services 29
30 Side-effects to Avoid Core A L1 lower level cache(s) L1 Core B false & true sharing Application (Producer) High latency memory operation Analysis (Consumer) Cache line ping-ponging Kathryn McKinley Towards Parallel, Scalable VM Services 30
31 Cache-friendly Asymmetric Buffering Lock-free communication channel between application and analysis thread Cache-friendly asymmetric buffering Actively avoids microarchitectural side-effects Enqueue light-weight instrumentation produces one record at time Dequeue consumes one chunk (fraction of a buffer) at a time Kathryn McKinley Towards Parallel, Scalable VM Services 31
32 Cache-friendly Asymmetric Buffering Core A L1 lower level cache(s) L1 Core B Application (Producer) Analysis (Consumer) application application writes here analyzer waits for application here buffer analyzer analyzer reads here 16 slots on the buffer 4 chunks, 4 slot on each chunk L1 size == chunk size chunk Kathryn McKinley Towards Parallel, Scalable VM Services 32
33 Cache-friendly Asymmetric Buffering Core A L1 lower level cache(s) Application (Producer) L1 Core B Analysis (Consumer) application buffer analyzer chunk Delay consumer dequeue operation until cache line is flushed 2 chunks away (smiley location) Analyzer operates one chunk at a time chunk_size > L1 size In practice, chunk_size >= 2 * L1 works well. Kathryn McKinley Towards Parallel, Scalable VM Services 33
34 Cache-friendly Asymmetric Buffering Core A L1 lower level cache(s) L1 Core B Application 4 0 (Producer) Analysis (Consumer) application analyzer application blocks only when buffer is full waiting until two more chunks are available Kathryn McKinley Towards Parallel, Scalable VM Services 34
35 Cache-friendly Asymmetric Buffering Producer (sees buffer) while (*bufptr!= 0) { } if (*bufptr == MAGIC) bufptr = buffer; if (*bufptr!= 0) block(); *bufptr++ = data; Consumer (sees chunk) while (app_is_running) { } index = index_of(chunk_num+2); while (buffer[index] == 0) spin_or_sleep(); consume (chunk_num); chunk_num = NEXT(chunk_num); MAGIC Consumer only spins here producer may spin on bufptr, while consumer may spin on buffer Producer code common case is 6 instructions in x86. Kathryn McKinley Towards Parallel, Scalable VM Services 35
36 Framework Provides Cache-friendly Asymmetric Buffering (CAB) Minimizes microarchitectural side-effects Quickly offloads event data from application s critical path Configurable parameters for optimization buffer size & chunk size Various collection mode Exhaustive mode Sampling mode Works on various threading model N:M (green) threading model native threading model Kathryn McKinley Towards Parallel, Scalable VM Services 36
37 Evaluation 3 different CMP processors 8KB L1 32KB 32KB 32KB 32KB 32KB 32KB 32KB 32KB 512KB L2 4MB L2 4MB L2 256K B 256K B 256K B 256K B system bus 8MB L3 Pentium 4 w/ hyperthreading Core 2 Quad Core i7 Kathryn McKinley Towards Parallel, Scalable VM Services 37
38 Evaluation Jikes RVM (2 different threading models) N:M threading (Jikes RVM 2.9.2) Native threading (Jikes RVM 3.0.1) Reference Dynamic Analysis Implementation Method counting Call graph Call tree profiling Path profiling Cache simulator using load/store events Benchmarks DaCapo, SPEC JVM 98 benchmark suites Parameters buffer size = 2MB, chunk size = 128KB Kathryn McKinley Towards Parallel, Scalable VM Services 38
39 Call Graph Profiling GeoMean Overhead (%) Core-i7 Core-2 Pentium-4 Instrumentation Enqueue Enqueue+Dequeue Concurrent (N:M) Sequential Instrumentation Overhead Bar 1 Bar1 Collect event data and write into a single word. No analysis thread Kathryn McKinley Towards Parallel, Scalable VM Services 39
40 Call Graph Profiling GeoMean Overhead (%) Instrumentation Enqueue Enqueue+Dequeue Concurrent (N:M) Sequential Core-i7 Core-2 Pentium-4 Enqueueing Overhead (Bar2 - Bar 1) Bar2 Collect event data and write into the buffer. No analysis thread Kathryn McKinley Towards Parallel, Scalable VM Services 40
41 Call Graph Profiling GeoMean Overhead (%) Instrumentation Enqueue Enqueue+Dequeue Concurrent (N:M) Sequential Core-i7 Core-2 Pentium-4 Communication Overhead (Bar3 Bar 2) Bar3 Analysis thread dequeues and write it into a single word. Kathryn McKinley Towards Parallel, Scalable VM Services 41
42 Call Graph Profiling GeoMean Overhead (%) Instrumentation Enqueue Enqueue+Dequeue Concurrent (N:M) Sequential Core-i7 Core-2 Pentium-4 Analysis (data processing) Overhead (Bar 5 Bar 1) Bar4 Concurrent Analysis Bar5 Sequential Analysis Kathryn McKinley Towards Parallel, Scalable VM Services 42
43 Call Graph Profiling GeoMean Overhead (%) Instrumentation Enqueue Enqueue+Dequeue Concurrent (N:M) Sequential Core-i7 Core-2 Pentium-4 Overhead reduction with Concurrent Analysis (Bar 5 Bar 4) Bar4 Concurrent Analysis Bar5 Sequential Analysis Kathryn McKinley Towards Parallel, Scalable VM Services 43
44 GeoMean Overhead (%) Path Profiling Core-i7 Core-2 Pentium-4 Instrumentation Enqueue Enqueue+Dequeue Concurrent (N:M) Sequential Overhead reduction with Concurrent Analysis (Bar 5 Bar 4) More data & computation than call graph Kathryn McKinley Towards Parallel, Scalable VM Services 44
45 Path Profiling Multithreaded Benchmarks Overhead (%) Instrumentation Enqueue Enqueue+Dequeue Concurrent (N:M) Sequential 0.00 mtrt hsqldb lusearch xalan Core 2 Multi-threaded benchmarks Kathryn McKinley Towards Parallel, Scalable VM Services 45
46 Concurrent Dynamic Analysis Framework Conclusions Framework eases implementation of many client analyses CAB efficiently transfers application data to analysis thread avoiding microarchitectural side-effects Framework efficiently utilizes extra cycles to perform dynamic analysis concurrently Kathryn McKinley Towards Parallel, Scalable VM Services 46
47 Related Work Concurrent Lock-free Queue FastForward single-producer & singleconsumer [Giacomoni et al. 09] Concurrent analysis for specific clients PiPA cache simulator [Zhao et al. 08] Shadow process approach Shadow profiling [Moseley et al. 07] SuperPin [Wallace et al. 07] Kathryn McKinley Towards Parallel, Scalable VM Services 47
48 Are we finished with CMP efficient buffering? Kathryn McKinley Towards Parallel, Scalable VM Services 48
49 Are we finished with CMP efficient buffering? Not yet Kathryn McKinley Towards Parallel, Scalable VM Services 49
50 Are we finished with CMP efficient buffering? Not yet parallel analysis memory scalable self-tuning parameters Kathryn McKinley Towards Parallel, Scalable VM Services 50
51 There is some hope but we need many such base mechanisms Kathryn McKinley Towards Parallel, Scalable VM Services 51
52 Software Challenges and Opportunities Communication (efficient coherency) Analysis (off critical path, new analyses) GC (concurrent, parallel, high throughput) JIT (concurrent, parallel, more aggressive) Heterogeneity (exploit it) Memory (PCM, bandwidth limits) Kathryn McKinley Towards Parallel, Scalable VM Services 52
53 Hardware Challenges and Opportunities Heterogeneity Tune cores to ubiquitous loads? Specialize for ubiquitous loads? Coherence SMT coherency does not scale software guarantees for simplified protocols? Memory/Cache Exploit access behavior of managed languages Kathryn McKinley Towards Parallel, Scalable VM Services 53
54 The 21 st Century Virtuous Cycle? Questions? More Cores Chip Multiproccesors CMP? Scalable Software Scalable Apps + Scalable Runtime Kathryn McKinley Towards Parallel, Scalable VM Services 54
55 I love my job Because when I fail, I get better Kathryn McKinley Towards Parallel, Scalable VM Services 55
56 Failures Rejected: jobs (all) Failed: my Rice PhD qualifying exam Rejected: jobs (8 of 11) Rejected: my first three grant applications Bad teaching evaluations Rejected 2 times: my most cited paper Rejected: jobs (some) Rejected: papers, grants, papers, grants, Kathryn McKinley Towards Parallel, Scalable VM Services 56
57 Processor Technologies Processor µarch µprocessor and Power Process # of # of Thermal Design Power (TDP) or chip power budget The amount of power consumed without exceeding the maximum junction temperature Power measurement Hall effect current sensor on 12V line driving the chip Sampling rate 50Hz nm Cores Threads Clock GHz LLC MB TDP W Release Date Pentium 4 NetBurst Northwood May 03 Core 2 Duo E6600 Core Conroe Jul 06 Core 2 Quad Q6600 Core Kentsfield Jan 07 Core i7 920 Nehalem Bloomfield Nov 08 Atom 230 Atom Diamondville Jun 08 Core 2 Duo E7600 Core Wolfdale May 09 Atom D510 Atom Pineview Dec 09 Core i5 670 Nehalem Clarkdale Jan 10 57
58 Native and Java Benchmarks 61 benchmarks from six suites Native (Fortran/C/C++) single-threaded (NST) SPEC CINT2006 (12) SPEC CFP2006 (15) Native multithreaded benchmark (NMT) PARSEC (11) Java single-threaded benchmarks (JST) SPEC JVM98 (6) DaCapo MR2 (2) DaCapo-9.12 (2) Java multithreaded benchmarks (JMT) SPEC JVM98 (1) DaCapo-9.12 (11) PJBB2005 (1) 58
59 Compiler, JVMs, OS, and Performance Intel compiler v11.1 with -O3 for NST Gnu gcc compiler v4.4.1 with -O3 for NMT Java virtual machines Sun (Oracle) HotSpot build 16.3-b01 Oracle JRockit build R IBM J9 build pxi3260sr8 We measure and report the 5 th iteration Operating system 32-bit Ubuntu 9.10 Karmic Linux kernel version Performance Normalized execution time to Atom Diamondville (45nm) Java with HotSpot default 59
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