Communicate or coordinate or perhaps no signicant need

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Unformatted text preview: ess significant terms! •  In this class, we’ll mainly be concerned with primary effects (asymptotic analysis)! •  In the real world, secondary effects are also often worth paying attention to (after the primary ones)! UW, Autumn 1999! CSE 373 – Data Structures and Algorithms! Brad Chamberlain! “Returning to the present...” In Parallel Compu3ng, Constants Maher •  AsymptoEc analysis (big- O notaEon) is crucial in parallel compuEng, as in tradiEonal compuEng •  However, constant factors also maber –  in parEcular, we’ll be running on a constant number of processors •  anecdote from computaEonal chemist colleague –  also, since performance is a primary moEvator for parallel compuEng, we typically want to squeeze out as many overheads as possible CSEP 524: Parallel ComputaEon Winter 2013: Chamberlain 53 Measuring Parallel Computa3ons (Directly) Timings: How long did the program take to run? –  typically measured in wallclock seconds (or fracEons thereof) Performance: At what rate is the program running? –  e.g., FLOPS (floaEng point operaEons per second) •  or simply OPS –  or something more domain- specific: •  graph codes: TEPS (traversed edges per second) •  memory bandwidth: GB/s (gigabytes per second) •  table updates: GUPS (giga- updates per second) CSEP 524: Parallel ComputaEon Winter 2013: Chamberlain 54 Measuring Parallel Computa3ons (Rela3vely) Speedup: How does the parallel execuEon compare to a serial execuEon? Speedupp = Tserial / Tp Linear/Ideal Speedup: Speedupp = p CSEP 524: Parallel ComputaEon Winter 2013: Chamberlain 55 Sample Speedup Graph CSEP 524: Parallel ComputaEon Winter 2013: Chamberlain 56 Compu3ng Speedup: The Baseline A key issue: What to use for the serial Eming? –  some opEons: •  the parallel code running using 1 task/processor? •  a serial implementaEon of the same algorithm? •  the best serial implementaEon available? –  The last is the most ideal/valuable •  e.g., if...
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