Model-Guided Empirical Optimization for Multimedia Extension Architectures: A Case Study

Compiler technology for multimedia extensions must effectively utilize not only the SIMD compute engines but also the various levels of the memory hierarchy: superword registers, multi-level caches and TLB. In this paper, we describe a compiler that combines optimization across all levels of the memory hierarchy with automatic generation of SIMD code for multimedia extensions. At the high-level, model-guided empirical optimization is used to transform code to optimize for all levels of the memory hierarchy. This compiler interacts with a backend compiler exploiting superword-level parallelism that takes sequential code as input and produces SIMD code. This paper discusses how we have combined these technologies into a single framework. Through a case study with matrix multiply, we observe performance results that outperform the hand-tuned Intel MKL library, and achieve performance that is within 4% of the ATLAS self-tuning library with architectural defaults and more than 4X faster than the native Intel compiler.