CUDAsap: Statically-Determined Execution Statistics as Alternative to Execution-Based Profiling

Abstract

Today a variety of different GPU types exists, raising questions regarding high-level tasks such as provisioning and scheduling. To predict execution time on different GPU types accurately, we propose a method to obtain execution statistics based on compile-time static code analysis, in which the control flow graph for the code's basic blocks is determined. This graph is represented as an adjacency matrix and used in a system of linear equations to calculate the basic block execution frequencies. Kernel execution itself is not necessary for this analysis. We analyze the proposed method for five different benchmark suites, showing that 76 out of 79 evaluated kernels can be analyzed with an average error of 0.4 %, primarily due to different LLVM versions, with an average prediction time of 203.96 ms. Furthermore, repetitive kernels make memoization effective, and the underlying analysis is largely independent of problem size.