CHiP: A Profiler to Measure the Effect of Cache Contention on Scalability

Programmers are looking for ways to exploit the multi-core processors which have become commonplace today. One of the options available is to parallelize the existing serial programs using frameworks like OpenMP etc. However, such parallelization does not always yield the speedup expected by the programmer. This is due to various reasons, one of which is the bottleneck presented by the memory system. Carefully optimized serial algorithms fit into most of the cache available, yet these cache optimized serial algorithms might have the worst speedup when parallelized. We present an efficient method to identify such cases and determine whether a serial algorithm's use of shared memory caches will seriously impact its parallel execution. This can help a programmer adjust their program's cache usage. We demonstrate the effect in isolation with a parallelized synthetic micro-benchmark which uses varying fractions of the shared cache. We also present the results of our analysis of the NAS Parallel Benchmark suite and the Rodinia benchmark suite.