PPT-Multicore: Performance Prediction of OpenMP applications using Reuse Profiles and Analytical Modeling

TL;DR

PPT-Multicore is an analytical framework that predicts the performance of OpenMP applications on multicore processors by using reuse profiles and cache modeling, achieving high accuracy in cache hit rate and runtime predictions.

Contribution

It introduces a novel reuse profile-based cache and performance prediction model for multicore architectures, validated on real benchmarks with high accuracy.

Findings

Predicts cache hit rates with 1.23% error

Predicts runtime with 9.08% error

Validates on PolyBench and PARSEC benchmarks

Abstract

We present PPT-Multicore, an analytical model embedded in the Performance Prediction Toolkit (PPT) to predict parallel application performance running on a multicore processor. PPT-Multicore builds upon our previous work towards a multicore cache model. We extract LLVM basic block labeled memory trace using an architecture-independent LLVM-based instrumentation tool only once in an application's lifetime. The model uses the memory trace and other parameters from an instrumented sequentially executed binary. We use a probabilistic and computationally efficient reuse profile to predict the cache hit rates and runtimes of OpenMP programs' parallel sections. We model Intel's Broadwell, Haswell, and AMD's Zen2 architectures and validate our framework using different applications from PolyBench and PARSEC benchmark suites. The results show that PPT-Multicore can predict cache hit rates with an overall average error rate of 1.23% while predicting the runtime with an error rate of 9.08%.