Extended performance accounting using Valgrind tool

Abstract

Modern workloads, parallel or sequential, usually suffer from insufficient memory and computing performance. Common trends to improve workload performance include the utilizations of complex functional units or coprocessors, which are able not only to provide accelerated computations but also independently fetch data from memory generating complex address patterns, with or without support of control flow operations. Such coprocessors usually are not adopted by optimizing compilers and should be utilized by special application interfaces by hand. On the other hand, memory bottlenecks may be avoided with proper use of processor prefetch capabilities which load necessary data ahead of actual utilization time, and the prefetch is also adopted only for simple cases making programmers to do it usually by hand. As workloads are fast migrating to embedded applications a problem raises how to utilize all hardware capabilities for speeding up workload at moderate efforts. This requires precise analysis of memory access patterns at program run time and marking hot spots where the vast amount of memory accesses is issued. Precise memory access model can be analyzed via simulators, for example Valgrind, which is capable to run really big workload, for example neural network inference in reasonable time. But simulators and hardware performance analyzers fail to separate the full amount of memory references and cache misses per particular modules as it requires the analysis of program call graph. We are extending Valgrind tool cache simulator, which allows to account memory accesses per software modules and render realistic distribution of hot spot in a program. Additionally the analysis of address sequences in the simulator allows to recover array access patterns and propose effective prefetching schemes. Motivating samples are provided to illustrate the use of Valgrind tool.

Prombles in programming 2021; 2: 54-62