Mozart - Efficient Composition of Library Functions for Heterogeneous Execution.

Current processor trend is to couple a commodity processor with a GPU, a co-processor, or an accelerator. To unleash the full computational power of such heterogeneous systems is a daunting task: programmers often resort to heterogeneous scheduling runtime frameworks that use device specific library routines. However, highly-tuned libraries do not compose very well across heterogeneous architectures. That is, important performance-oriented optimizations such as data locality and reuse "across" library calls is not fully exploited. In this paper, we present a framework, called Mozart, to extend existing library frameworks to efficiently compose a sequence of library calls for heterogeneous execution. Mozart consists of two components: library description (LD) and library composition runtime. We advocate library writers to wrap existing libraries using LD in order to provide their performance parameters on heterogeneous cores, no programmer intervention is necessary. Our runtime performs composition of libraries via task-fission, load balances among heterogeneous cores using information from LD, and automatically adapts to runtime behavior of an application. We evaluate Mozart on a Xeon + 2 Xeon Phi system using the High Performance Linpack benchmark which is the most popular benchmark to rank supercomputers in TOP500 and show GFLOPS improvement of 31.7% over MKL with Automatic Offload and 6.7% over hand-optimized ninja code.