Characterising radio telescope software with the Workload Characterisation Framework [IMA]

We present a modular framework, the Workload Characterisation Framework (WCF), that is developed to reproducibly obtain, store and compare key characteristics of radio astronomy processing software. As a demonstration, we discuss the experiences using the framework to characterise a LOFAR calibration and imaging pipeline.

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Y. Grange, R. Lakhoo, M. Petschow, et. al.
Mon, 5 Dec 16

Comments: 4 pages, 4 figures; to be published in ADASS XXVI (held October 16-20, 2016) proceedings. See this http URL for the poster


PageRank Pipeline Benchmark: Proposal for a Holistic System Benchmark for Big-Data Platforms [CL]

The rise of big data systems has created a need for benchmarks to measure and compare the capabilities of these systems. Big data benchmarks present unique scalability challenges. The supercomputing community has wrestled with these challenges for decades and developed methodologies for creating rigorous scalable benchmarks (e.g., HPC Challenge). The proposed PageRank pipeline benchmark employs supercomputing benchmarking methodologies to create a scalable benchmark that is reflective of many real-world big data processing systems. The PageRank pipeline benchmark builds on existing prior scalable benchmarks (Graph500, Sort, and PageRank) to create a holistic benchmark with multiple integrated kernels that can be run together or independently. Each kernel is well defined mathematically and can be implemented in any programming environment. The linear algebraic nature of PageRank makes it well suited to being implemented using the GraphBLAS standard. The computations are simple enough that performance predictions can be made based on simple computing hardware models. The surrounding kernels provide the context for each kernel that allows rigorous definition of both the input and the output for each kernel. Furthermore, since the proposed PageRank pipeline benchmark is scalable in both problem size and hardware, it can be used to measure and quantitatively compare a wide range of present day and future systems. Serial implementations in C++, Python, Python with Pandas, Matlab, Octave, and Julia have been implemented and their single threaded performance has been measured.

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P. Dreher, C. Byun, C. Hill, et. al.
Tue, 8 Mar 16

Comments: 9 pages, 7 figures, to appear in IPDPS 2016 Graph Algorithms Building Blocks (GABB) workshop

Separable projection integrals for higher-order correlators of the cosmic microwave sky: Acceleration by factors exceeding 100 [CL]

We study the optimisation and porting of the “Modal” code on Intel(R) Xeon(R) processors and/or Intel(R) Xeon Phi(TM) coprocessors using methods which should be applicable to more general compute bound codes. “Modal” is used by the Planck satellite experiment for constraining general non-Gaussian models of the early universe via the bispectrum of the cosmic microwave background. We focus on the hot-spot of the code which is the projection of bispectra from the end of inflation to spherical shell at decoupling which defines the CMB we observe. This code involves a three-dimensional inner product between two functions, one of which requires an integral, on a non-rectangular sparse domain. We show that by employing separable methods this calculation can be reduced to a one dimensional summation plus two integrations reducing the dimensionality from four to three. The introduction of separable functions also solves the issue of the domain allowing efficient vectorisation and load balancing. This method becomes unstable in certain cases and so we present a discussion of the optimisation of both approaches. By making bispectrum calculations competitive with those for the power spectrum we are now able to consider joint analysis for cosmological science exploitation of new data. We demonstrate speed-ups of over 100x, arising from a combination of algorithmic improvements and architecture-aware optimizations targeted at improving thread and vectorization behaviour. The resulting MPI/OpenMP code is capable of executing on clusters containing Intel(R) Xeon(R) processors and/or Intel(R) Xeon Phi(TM) coprocessors, with strong-scaling efficiency of 98.6% on up to 16 nodes. We find that a single coprocessor outperforms two processor sockets by a factor of 1.3x and that running the same code across a combination of processors and coprocessors improves performance-per-node by a factor of 3.38x.

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J. Briggs, J. Jaykka, J. Fergusson, et. al.
Fri, 3 Apr 15

Comments: N/A

Architecture, implementation and parallelization of the software to search for periodic gravitational wave signals [CL]

The parallelization, design and scalability of the \sky code to search for periodic gravitational waves from rotating neutron stars is discussed. The code is based on an efficient implementation of the F-statistic using the Fast Fourier Transform algorithm. To perform an analysis of data from the advanced LIGO and Virgo gravitational wave detectors’ network, which will start operating in 2015, hundreds of millions of CPU hours will be required – the code utilizing the potential of massively parallel supercomputers is therefore mandatory. We have parallelized the code using the Message Passing Interface standard, implemented a mechanism for combining the searches at different sky-positions and frequency bands into one extremely scalable program. The parallel I/O interface is used to escape bottlenecks, when writing the generated data into file system. This allowed to develop a highly scalable computation code, which would enable the data analysis at large scales on acceptable time scales. Benchmarking of the code on a Cray XE6 system was performed to show efficiency of our parallelization concept and to demonstrate scaling up to 50 thousand cores in parallel.

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G. Poghosyan, S. Matta, A. Streit, et. al.
Tue, 3 Feb 15

Comments: 11 pages, 9 figures. Submitted to Computer Physics Communications