XPlacer: Automatic Analysis of Data Access Patterns on Heterogeneous CPU/GPU Systems

This paper presents XPlacer, a framework to automatically analyze problematic data access patterns in C++ and CUDA code. XPlacer records heap memory operations in both host and device code for later analysis. To this end, XPlacer instruments read and write operations, function calls, and kernel launches. Programmers mark points in the program execution where the recorded data is analyzed and anomalies diagnosed. XPlacer reports data access anti-patterns, including alternating CPU/GPU accesses to the same memory, memory with low access density, and unnecessary data transfers. The diagnostic also produces summative information about the recorded accesses, which aids users in identifying code that could degrade performance. The paper evaluates XPlacer using LULESH, a Lawrence Livermore proxy application, Rodina benchmarks, and an implementation of the Smith-Waterman algorithm. XPlacer diagnosed several performance issues in these codes. The elimination of a performance problem in LULESH resulted in a 3x speedup on a heterogeneous platform combining Intel CPUs and Nvidia GPUs.