Computer Science and Engineering Parallel and Distributed Processing CSE 8380 February Session 6

Computer Science and Engineering Performance Evaluation Grosch’s Law Moore’s Law Von Neumann’s Bottlneck Parallelism Speedup Amdahl’s Law The Gustafson-Barsis Law Benchmarks Contents

Computer Science and Engineering Grosch’s Law (1960s) “To sell a computer for twice as much, it must be four times as fast” Vendors skip small speed improvements in favor of waiting for large ones Buyers of expensive machines would wait for a twofold improvement in performance for the same price.

Computer Science and Engineering Moore’s Law Gordon Moore (cofounder of Intel) Processor performance would double every 18 months This prediction has held for several decades Unlikely that single-processor performance continues to increase indefinitely

Computer Science and Engineering Von Neumann’s bottleneck Great mathematician of the 1940s and 1950s Single control unit connecting a memory to a processing unit Instructions and data are fetched one at a time from memory and fed to processing unit Speed is limited by the rate at which instructions and data are transferred from memory to the processing unit.

Computer Science and Engineering Problem Assume that a switching component such as a transistor can switch in zero time. We propose to construct a disk- shaped computer chip with such a component. The only limitation is the time it takes to send electronic signals from one edge of the chip to the other. Make the simplifying assumption that electronic signals travel 300,000 kilometers per second. What must be the diameter of a round chip so that it can switch 10 9 times per second? What would the diameter be if the switching requirements were time per second?

Computer Science and Engineering Parallelism Multiple CPUs Within the CPU One Pipeline Multiple pipelines

Computer Science and Engineering Superscalar Parallelism Scheduling

Computer Science and Engineering Past Trends in Parallel Architecture (inside the box) Completely custom designed components (processors, memory, interconnects, I/O) Longer R&D time (2-3 years) Expensive systems Quickly becoming outdated Bankrupt companies!!

Computer Science and Engineering New Trends in Parallel Architecture (outside the box) Advances in commodity processors and network technology Network of PCs and workstations connected via LAN or WAN forms a Parallel System Network Computing Compete favorably (cost/performance) Utilize unused cycles of systems sitting idle

Computer Science and Engineering Speedup S = Speed(new) / Speed(old) S = Work/time(new) / Work/time(old) S = time(old) / time(new) S = time(before improvement) / time(after improvement)

Computer Science and Engineering Speedup Time (one CPU): T(1) Time (n CPUs): T(n) Speedup: S S = T(1)/T(n)

Computer Science and Engineering Amdahl’s Law The performance improvement to be gained from using some faster mode of execution is limited by the fraction of the time the faster mode can be used

Computer Science and Engineering 20 hours 200 miles A B Walk 4 miles /hour Bike 10 miles / hour Car-1 50 miles / hour Car miles / hour Car miles /hour must walk Example

Computer Science and Engineering 20 hours 200 miles A B Walk 4 miles /hour  = 70 hours S = 1 Bike 10 miles / hour  = 40 hours S = 1.8 Car-1 50 miles / hour  = 24 hours S = 2.9 Car miles / hour  = hours S = 3.2 Car miles /hour  = hours S = 3.4 must walk Example

Computer Science and Engineering Amdahl’s Law (1967)  : The fraction of the program that is naturally serial (1-  ): The fraction of the program that is naturally parallel

Computer Science and Engineering S = T(1)/T(N) T(N) = T(1)  + T(1)(1-  ) N S = 1  + (1-  ) N = N  N + (1-  )

Computer Science and Engineering Amdahl’s Law

Computer Science and Engineering Gustafson-Barsis Law N &  are not independent from each other T(N) = 1 T(1) =  + (1-  ) N S = N – (N-1)   : The fraction of the program that is naturally serial

Computer Science and Engineering Gustafson-Barsis Law

Computer Science and Engineering

Distributed Computing Performance Single Program Performance Multiple Program Performance

Computer Science and Engineering

Benchmark Performance Serial Benchmarks Parallel Benchmarks PERFECT Benchmarks NAS Kernel The SLALOM The Golden Bell Prize WebSTONE for the Web Performance Comparisons