A Simple Synchronous Distributed-Memory Algorithm for the HPCC RandomAccess Benchmark

An alternative algorithm is presented which treats the collection of P processors as a hypercube, aggregating data so that larger messages are sent, and routing individual datums through dimensions of the hypercube to their destination processor, thus enabling better performance on large numbers of processors.

Abstract

The RandomAccess benchmark as defined by the High Performance Computing Challenge (HPCC) tests the speed at which a machine can update the elements of a table spread across global system memory, as measured in billions (giga) of updates per second (GUPS). The parallel implementation provided by HPCC typically performs poorly on distributed-memory machines, due to updates requiring numerous small point-to-point messages between processors. We present an alternative algorithm which treats the collection of P processors as a hypercube, aggregating data so that larger messages are sent, and routing individual datums through dimensions of the hypercube to their destination processor. The algorithm's computation (the GUP count) scales linearly with P while its communication overhead scales as log2(P), thus enabling better performance on large numbers of processors. The new algorithm achieves a GUPS rate of 19.98 on 8192 processors of Sandia's Red Storm machine, compared to 1.02 for the HPCCprovided algorithm on 10350 processors. We also illustrate how GUPS performance varies with the benchmark's specification of its "look-ahead" parameter. As expected, parallel performance degrades for small look-ahead values, and improves dramatically for large values.