@MISC{Li_emergingmultimedia, author = {Man-lap Li}, title = {EMERGING MULTIMEDIA APPLICATIONS BY}, year = {} }
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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 multicore 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 thesis makes three 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 parallelism using POSIX threads and data-level parallelism using Intel’s SSE2 instructions and vector extensions. Second, the thesis 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.