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Graph engines have been used in many applications to handle big graphs efficiently. The majority of the research to improve their performance has focused primarily on the design of efficient graph processing. This paper claims, however, the focus should be given also to graph storage design. This is because good storage design can improve both CPU performance and I/O performance of graph engines. In this paper, we propose an efficient data layout for single-machine based graph engines. We identify the common node access pattern of the graph algorithms running on single-machine based graph engines. Based on this finding, we propose the breadth-first (BF) data layout which places the nodes processed together in the same or adjacent storage space so that they can be accessed together as much as possible. The experimental results show that the BF data layout improves both CPU and I/O performances significantly in all single-machine based graph engines. [ABSTRACT FROM AUTHOR]

In the past decade, high performance compute capabilities exhibited by heterogeneous GPGPU platforms have led to the popularity of data parallel programming languages such as CUDA and OpenCL. Developing high performance parallel programming solutions using such languages involve a steep learning curve due to the complexity of the underlying heterogeneous compute devices and their impact on performance. This has led to the emergence of several High Performance Computing frameworks which provide high-level abstractions for easing the development of data-parallel applications on heterogeneous platforms. However, the scheduling decisions undertaken by such frameworks only exploit coarse-grained concurrency in data parallel applications. In this paper, we propose PySchedCL, a framework which explores fine-grained concurrency aware scheduling decisions that harness the power of heterogeneous CPU/GPU architectures efficiently. We showcase the efficacy of such scheduling mechanisms over existing coarse-grained dynamic scheduling schemes by conducting extensive experimental evaluations for a diverse set of popular Deep Learning benchmarks. [ABSTRACT FROM AUTHOR]