The ALPBench Benchmark Suite for Multimedia Applications

Abstract

Multimedia applications are becoming increasingly important for a large class of general-purpose processors. Contemporary media applications are highly complex and demand high performance. A distinctive feature of these applications is that they have significant parallelism, including thread- , data-, and instruction- level parallelism, that is potentially well-aligned with the increasing parallelism supported by emerging multi-core architectures. Designing systems to meet the demands of these applications therefore requires a benchmark suite comprising these complex applications and that exposes the parallelism present in them.

This paper makes two main contributions. First, it presents ALPBench, a publicly released benchmark suite that pulls together five complex media applications from various sources: speech recognition (CMU Sphinx 3.3), face recognition (CSU), ray tracing (Tachyon), MPEG-2 encode (MSSG), and MPEG-2 decode (MSSG). We have modified the original applications to expose thread-level and data-level parallelism using POSIX threads and Intel's SSE2 instructions respectively. Second, the paper provides a performance characterization of the ALPBench benchmarks, with a focus on parallelism. Such a characterization is useful for architects and compiler writers for designing systems and compiler optimizations for these applications.