A Performance Model for Memory Bandwidth Constrained Applications on Graphics Engines

Graphics engines are excellent execution platforms for high-throughput computations that exploit a large degree of available parallelism. The achieved performance is, however, highly dependent on the access patterns that the applicationimposes on the memory subsystem. Here, we propose an analytic model that helps improve the understanding of the performance of memory-limited kernels that employ randommemory access schemes, especially as impacted by cache andvarious configuration parameters that can be used to tunekernel execution, such as the number of blocks and the number of threads per block. The analytic model is first explored through the use of a synthetic micro-benchmark, which is then followed by an empirical validation using a pair of production applications used in computational biology.