Performance Analysis of Traditional and Data-Parallel Primitive Implementations of Visualization and Analysis Kernels

TL;DR

This paper compares traditional and data-parallel implementations of visualization and analysis kernels on multi-core CPUs by analyzing hardware performance counters, revealing performance differences not evident from runtime alone.

Contribution

It provides a detailed hardware performance counter analysis of traditional versus data-parallel kernel implementations on modern multi-core platforms.

Findings

Data-parallel approach shows different cache utilization patterns.

Performance differences are evident in hardware counters, not just runtime.

Insights can guide optimization of visualization kernels.

Abstract

Measurements of absolute runtime are useful as a summary of performance when studying parallel visualization and analysis methods on computational platforms of increasing concurrency and complexity. We can obtain even more insights by measuring and examining more detailed measures from hardware performance counters, such as the number of instructions executed by an algorithm implemented in a particular way, the amount of data moved to/from memory, memory hierarchy utilization levels via cache hit/miss ratios, and so forth. This work focuses on performance analysis on modern multi-core platforms of three different visualization and analysis kernels that are implemented in different ways: one is "traditional", using combinations of C++ and VTK, and the other uses a data-parallel approach using VTK-m. Our performance study consists of measurement and reporting of several different hardware performance counters on two different multi-core CPU platforms. The results reveal interesting performance differences between these two different approaches for implementing these kernels, results that would not be apparent using runtime as the only metric.