A Stream Processing Framework for On-Line Optimization of Performance and Energy Efficiency on Heterogeneous Systems

Modern processors have the potential of executing compute-intensive programs quickly and efficiently, but require applications to be adapted to their ever increasing parallelism. Here, heterogeneous systems add complexity by combining processing units with different characteristics. Scheduling should thus consider the performance of each processor as well as competing workloads and varying inputs. To assist programmers of stream processing applications in facing this challenge we present libHawaii, an open source library for cooperatively using all processors of heterogeneous systems easily and efficiently. It supports exploiting data flow, data element and task parallelism via pipelining, partitioning and demand-based allocation of consecutive work items. Scheduling is automatically adapted on-line to continuously optimize performance and energy efficiency. Our C++ library does not depend on specific hardware architectures or parallel computing frameworks. However, it facilitates maximizing the throughput of compatible GPUs by overlapping computations and memory transfers while maintaining low latencies. This paper describes the algorithms and implementation of libHawaii and demonstrates its usage on existing applications. We experimentally evaluate our library using two examples: General matrix multiplication (GEMM) is a simple yet important building block of many high-performance computing applications. Complementarily, the detection, extraction and matching of sparse image features exhibits greater complexity, including indeterministic memory access and synchronization.