MIPP: A microbenchmark suite for performance, power, and energy consumption characterization of GPU architectures

GPU-accelerated applications are becoming increasingly common in high-performance computing as well as in low-power heterogeneous embedded systems. Nevertheless, GPU programming is a challenging task, especially if a GPU application has to be tuned to fully take advantage of the GPU architectural configuration. Even more challenging is the application tuning by considering power and energy consumption, which have emerged as first-order design constraints in addition to performance. Solving bottlenecks of a GPU application such as high thread divergence or poor memory coalescing have a different impact on the overall performance, power and energy consumption. Such an impact also depends on the GPU device on which the application is run. This paper presents a suite of microbenchmarks, which are specialized chunks of GPU code that exercise specific device components (e.g., arithmetic instruction units, shared memory, cache, DRAM, etc.) and that provide the actual characteristics of such components in terms of throughput, power, and energy consumption. The suite aims at enriching standard profiler information and guiding the GPU application tuning on a specific GPU architecture by considering all three design constraints (i.e., power, performance, energy consumption). The paper presents the results obtained by applying the proposed suite to characterize two different GPU devices and to understand how application tuning may impact differently on them.