Why parallel arch.?

• Parallelism helps
• There are applications that can be parallelized easily
• There are important applications that require enormous amount of computation (10 GFLOPS to 1 TFLOPS)
  • NASA taps SGI, Intel for Supercomputers: 20 512p SGI Altix using Itanium 2 (http://zdnet.com.com/2100-1103_2-5286156.html) [27th July, 2004]
• There are important applications that need to deliver high throughput

Why study it?

• Parallelism is ubiquitous
  • Need to understand the design trade-offs
  • Microprocessors are now multiprocessors (more later)
  • Today a computer architect’s primary job is to find out how to efficiently extract parallelism
• Get involved in interesting research projects
  • Make an impact
  • Shape the future development
  • Have fun

Performance metrics

• Need benchmark applications
  • SPLASH (Stanford ParalleL Applications for SHared memory)
  • SPEC (Standard Performance Evaluation Corp.) OMP
  • ScaLAPACK (Scalable Linear Algebra PACKage) for message-passing machines
  • TPC (Transaction Processing Performance Council) for database/transaction processing performance
  • NAS (Numerical Aerodynamic Simulation) for aerophysics applications
    • NPB2 port to MPI for message-passing only
  • PARKBENCH (PARallel Kernels and BENCHmarks) for message-passing only 
• Comparing two different parallel computers
  • Execution time is the most reliable metric
  • Sometimes MFLOPS, GFLOPS, TFLOPS are used, but could be misleading
• Evaluating a particular machine
  • Use speedup to gauge scalability of the machine (provided the application itself scales)
  • Speedup(P) = Uniprocessor time/Time on P processors
  • Normally the input data set is kept constant when measuring speedup