Vector-Processing for Mobile Devices: Benchmark and Analysis

TL;DR

This paper introduces Swan, a comprehensive benchmark suite for evaluating vector processing in mobile devices, analyzing its performance, power, and energy impacts across diverse mobile workloads.

Contribution

The paper presents Swan, the first extensive benchmark suite for vector processing in mobile applications, along with a detailed analysis of its performance and energy characteristics.

Findings

Vectorized workloads increase cache pressure due to more memory requests.

Vector processing benefits are higher for low-precision, cache-friendly workloads.

Limited parallelism and strided memory access hinder scaling of vector processing.

Abstract

Vector processing has become commonplace in today's CPU microarchitectures. Vector instructions improve performance and energy which is crucial for resource-constraint mobile devices. The research community currently lacks a comprehensive benchmark suite to study the benefits of vector processing for mobile devices. This paper presents Swan-an extensive vector processing benchmark suite for mobile applications. Swan consists of a diverse set of data-parallel workloads from four commonly used mobile applications: operating system, web browser, audio/video messaging application, and PDF rendering engine. Using Swan benchmark suite, we conduct a detailed analysis of the performance, power, and energy consumption of vectorized workloads, and show that: (a) Vectorized kernels increase the pressure on cache hierarchy due to the higher rate of memory requests. (b) Vector processing is more beneficial for workloads with lower precision operations and higher cache hit rates. (c) Limited Instruction-Level Parallelism and strided memory accesses to multi-dimensional data structures prevent vector processing benefits from scaling with more SIMD functional units and wider registers. (d) Despite lower computation throughput than domain-specific accelerators, such as GPU, vector processing outperforms these accelerators for kernels with lower operation counts. Finally, we show five common computation patterns in mobile data-parallel workloads that dominate the execution time.