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Understanding Parallelism and the Limitations of Parallel Computing Understanding Parallelism: Sequential Work

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Understanding Parallelism and the Limitations of Parallel Computing Understanding Parallelism: Sequential Work Understanding Parallelism and the Limitations of Parallel Computing Understanding Parallelism: Sequential work After 16 time steps: 4 cars (c) RRZE 2013 Scalability Laws 2 Understanding Parallelism: Parallel work After 4 time steps: 4 cars “perfect speedup” (c) RRZE 2013 Scalability Laws 3 Understanding parallelism: Shared resources, imbalance shared resource Unused resources due to resource bottleneck and imbalance! Waiting for shared Waiting for resource synchronization (c) RRZE 2013 Scalability Laws 4 Limitations of Parallel Computing: Amdahl's Law Ideal world: All work is perfectly parallelizable serial serial serial serial Closer to reality: Purely serial parts serialseriell serial limit maximum speedup Reality is even worse: Communication and synchronization impede scalability even further (c) RRZE 2013 Scalability Laws 5 Limitations of Parallel Computing: Calculating Speedup in a Simple Model (“strong scaling”) T(1) = s+p = serial compute time (=1) parallelizable part: p = 1-s purely serial part s Parallel execution time: T(N) = s+p/N General formula for speedup: k T(1) 1 Amdahl's Law (1967) S p “strong scaling” 1s T(N) s N (c) RRZE 2013 Scalability Laws 6 Limitations of Parallel Computing: Amdahl's Law (“strong scaling”) . Reality: No task is perfectly parallelizable . Shared resources have to be used serially . Task interdependencies must be accounted for . Communication overhead (but that can be modeled separately) . Benefit of parallelization may be strongly limited . "Side effect": limited scalability leads to inefficient use of resources . Metric: Parallel Efficiency (what percentage of the workers/processors is efficiently used): S (N) (N) p p N . Amdahl case: 1 p s(N 1) 1 (c) RRZE 2013 Scalability Laws 7 Limitations of Parallel Computing: Adding a simple communication model for strong scaling T(1) = s+p = serial compute time parallelizable part: p = 1-s purely serial part s Model assumption: non- parallel: T(N) = s+p/N+Nk overlapping communication fraction k for messages communication per worker General formula for speedup: T(1) 1 S k p 1s T(N) s N Nk (c) RRZE 2013 Scalability Laws 8 Limitations of Parallel Computing: Amdahl's Law (“strong scaling”) . Large N limits 0 1 . at k=0, Amdahl's Law lim S p (N) predicts N s independent of N ! . at k≠0, our simple model k N 1 1 of communication S p (N) overhead yields a Nk beaviour of . Problems in real world programming . Load imbalance . Shared resources have to be used serially (e.g. IO) . Task interdependencies must be accounted for . Communication overhead (c) RRZE 2013 Scalability Laws 9 Limitations of Parallel Computing: Amdahl´s Law (“strong scaling”) + comm. model 10 9 8 7 6 s=0.01 5 s=0.1 S(N) s=0.1, k=0.05 4 3 2 1 0 1 2 3 4 5 6 7 8 9 10 # CPUs (c) RRZE 2013 Scalability Laws 10 Limitations of Parallel Computing: Amdahl´s Law (“strong scaling”) Parallel 100 efficiency: 90 <10% 80 ~50% 70 60 s=0.01 50 s=0.1 S(N) s=0.1, k=0.05 40 30 20 10 0 1 10 100 500 1000 # CPUs (c) RRZE 2013 Scalability Laws 11 Limitations of Parallel Computing: How to mitigate overheads . Communication is not necessarily purely serial . Non-blocking crossbar networks can transfer many messages concurrently – factor Nk in denominator becomes k (technical measure) . Sometimes, communication can be overlapped with useful work (implementation, algorithm): . Communication overhead may show a more fortunate behavior than Nk . "superlinear speedups“: data size per CPU decreases with increasing CPU count may fit into cache at large CPU counts (c) RRZE 2013 Scalability Laws 12 Limits of Scalability: Serial & Parallel fraction Serial fraction s may depend on . Program / algorithm . Non-parallelizable part, e.g. recursive data setup . Non-parallelizable IO, e.g. reading input data . Communication structure . Load balancing (assumed so far: perfect balanced) . … . Computer hardware . Processor: Cache effects & memory bandwidth effects . Parallel Library; Network capabilities; Parallel IO . … Determine s "experimentally": Measure speedup and fit data to Amdahl’s law – but that could be more complicated than it seems… (c) RRZE 2013 Scalability Laws 13 Scalability data on modern multi-core systems An example Scaling across nodes P P P P C C C C CP P CP P C C C C CP P CP P C P C P C P C P C C ChipsetC C C C 12 sockets ChipsetC C on node Chipset MemoryChipset Memory 12 cores on Memory socket Memory (c) RRZE 2013 Scalability Laws 14 Scalability data on modern multi-core systems The scaling baseline . Scalability presentations should be 2,0 intranode grouped according to the 1,5 memory- largest unit that the bound 1,0 scaling is based on code! Speedup Good 0,5 (the “scaling baseline”) scaling 0,0 across 1 2 4 sockets # CPUs . Amdahl model with communication: Fit 1 S(N) s 1s kN N internode to inter-node scalability numbers (N = # nodes, >1) (c) RRZE 2013 Scalability Laws 15 Application to “accelerated computing” . SIMD, GPUs, Cell SPEs, FPGAs, just any optimization… . Assume overall (serial, un-accelerated) runtime to be Ts=s+p=1 . Assume p can be accelerated and run α times faster. We neglect any additional cost (communication…) . To get a speedup of rα, how small must s be? Solve for s: 1 r 1 1 r s 1 s 1 s . At α=10 and r =0.9 (for an overall speedup of 9), we get s≈0.012, i.e. you must accelerate 98.8% of serial runtime! . Limited memory on accelerators may limit the achievable speedup (c) RRZE 2013 Scalability Laws 16 .
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