02-Machines and Models

What is the goal of parallel programming?
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1.CSC2/458 Parallel and Distributed Systems Machines and Models Sreepathi Pai January 23, 2018 URCS

2.Outline Recap Scalability Taxonomy of Parallel Machines Performance Metrics

3.Outline Recap Scalability Taxonomy of Parallel Machines Performance Metrics

4.Goals What is the goal of parallel programming?

5.Scalability Why is scalability important?

6.Outline Recap Scalability Taxonomy of Parallel Machines Performance Metrics

7.Speedup T1 Speedup(n) = Tn • T1 is time on one processor • Tn is time on n processors

8.Amdahl’s Law Let: • T1 be Tserial + Tparallelizable Tparallelizable • Tn is then Tserial + n , assuming perfect scalability Divide both terms T1 and Tn by T1 to obtain serial and parallelizable ratios. 1 Speedup(n) = rparallelizable rserial + n

9.Amdahl’s Law – In the limit 1 Speedup(∞) = rserial This is also known as strong scalability – work is fixed and number of processors is varied. What are the implications of this?

10.Scalability Limits Assuming infinite processors, what is the speedup if: • serial ratio rserial is 0.5 (i.e. 50%) • serial ratio is 0.1 (i.e. 10%) • serial ratio is 0.01 (i.e. 1%)

11.Current Top 5 supercomputers • Sunway TaihuLight (10.6M cores) • Tianhe 2 (3.1M cores) • Piz Daint (361K cores) • Gyoukou (19.8M cores) • Titan (560K cores) Source: Top 500

12.Weak Scalability • Work increases as number of processors increase • Parallel work should increase linearly with processors • Work W = αW + (1 − α)W • α is serial fraction of work • Scaled Work W = αW + n(1 − α)W • Empirical observation • Usually referred to as Gustafson’s Law Source: http://www.johngustafson.net/pubs/pub13/amdahl.htm

13.Outline Recap Scalability Taxonomy of Parallel Machines Performance Metrics

14.Organization of Parallel Computers Components of parallel machines: • Processing Elements • Memories • Interconnect • how processors, memories are connnected to each other

15.Flynn’s Taxonomy • Based on notion of “streams” • Instruction stream • Data stream • Taxonomy based on number of each type of streams • Single Instruction - Single Data (SISD) • Single Instruction - Multiple Data (SIMD) • Multiple Instruction - Single Data (MISD) • Multiple Instruction - Multiple Data (MIMD) Flynn, J., (1966), “http://ieeexplore.ieee.org/document/1447203/Very High Speed Computing Systems”, Proceedings of the IEEE

16.SIMD Implementations: Vector Machines The Cray-1 (circa 1977): • Vx – vector registers • 64 elements • 64-bits per element • Vector length register (Vlen) • Vector mask register Richard Russell, “The Cray-1 Computer System”, Comm. ACM 21,1 (Jan 1978), 63-72

17.Vector Instructions – Vertical 1 2 3 4 + 5 6 2 3 = 6 8 5 7 For 0 < i < Vlen: dst[i] = src1[i] + src2[i] • Most arithmetic instructions

18.Vector Instructions – Horizontal 1 = min( 1 2 3 4 ) For 0 < i < Vlen: dst = min(src1[i], dst) Note that dst is a scalar. • Mostly reductions (min, max, sum, etc.) • Not well supported • Cray-1 did not have this

19.Vector Instructions – Shuffle/Permute src 1 2 3 4 mask 0 3 1 1 dst 1 4 2 2 dst = shuffle(src1, mask) • Poor support on older implementations • Reasonably well-supported on recent implementations

20.Masking/Predication src1 6 5 7 2 g5mask 1 0 1 0 src1 6 5 7 2 * src2 1 4 2 2 = dst 6 ? 14 ? g5mask = gt(src1, 5) dst = mul(src1, src2, g5mask)

21.MISD - ? Flynn, J., (1966), “http://ieeexplore.ieee.org/document/1447203/Very High Speed Computing Systems”, Proceedings of the IEEE

22.What type of machine is this? Hyperthreaded Core Different colours in RAM indicate different instruction streams. Source: https://en.wikipedia.org/wiki/Hyper-threading

23.What type of machine is this? GPU Each instruction is 32-wide. Source: https://devblogs.nvidia.com/inside-pascal/

24.What type of machine is this? TPU Matrix Multiply Unit Source: https://cloud.google.com/blog/big-data/2017/05/an-in-depth-look-at-googles-first-tensor-processing-unit-tpu

25.TPU Overview Source: https://cloud.google.com/blog/big-data/2017/05/an-in-depth-look-at-googles-first-tensor-processing-unit-tpu

26.Modern Multicores • Multiple Cores (MIMD) • (Short) Vector Instruction Sets (SIMD) • MMX, SSE, AVX (Intel) • 3DNow (AMD) • NEON (ARM)

27.Outline Recap Scalability Taxonomy of Parallel Machines Performance Metrics

28.Metrics we care about • Latency • Time to complete task • Lower is better • Throughput • Rate of completing tasks • Higher is better • Utilization • Time “worker” (processor, unit) is busy • Higher is better • Speedup • Higher is better

29.Reducing Latency • Use cheap operations • Which of these operations are expensive? • Bitshift • Integer Divide • Integer Multiply • Latency fundamentally bounded by physics