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Problems in scientific computing, such as distributing large sparse matrix operations, have analogous formulations as hypergraph partitioning problems. A hypergraph is a generalization of a traditional graph wherein “hyperedges” may connect any number of nodes. As a result, hypergraph partitioning is an NP-Hard problem to both solve or approximate. State-of-the-art algorithms that solve this problem follow the multilevel paradigm, which begins by iteratively “coarsening” the input hypergraph to smaller problem instances that share key structural features. Once identifying an approximate problem that is small enough to be solved directly, that solution can be interpolated and refined to the original problem. While this strategy represents an excellent trade off between quality and running time, it is sensitive to coarsening strategy. In this work we propose using graph embeddings of the initial hypergraph in order to ensure that coarsened problem instances retrain key structural features. Our approach prioritizes coarsening within self-similar regions within the input graph, and leads to significantly improved solution quality across a range of considered hypergraphs. Reproducibility: All source code, plots and experimental data are available at https://sybrandt.com/2019/partition. [ABSTRACT FROM AUTHOR]

Data-parallel applications, such as data analytics, machine learning, and scientific computing, are placing an ever-growing demand on floating-point operations per second on emerging systems. With increasing integration density, the quest for energy efficiency becomes the number one design concern. While dedicated accelerators provide high energy efficiency, they are over-specialized and hard to adjust to algorithmic changes. We propose an architectural concept that tackles the issues of achieving extreme energy efficiency while still maintaining high flexibility as a general-purpose compute engine. The key idea is to pair a tiny 10kGE (kilo gate equivalent) control core, called Snitch, with a double-precision floating-point unit (FPU) to adjust the compute to control ratio. While traditionally minimizing non-floating-point unit (FPU) area and achieving high floating-point utilization has been a trade-off, with Snitch, we achieve them both, by enhancing the ISA with two minimally intrusive extensions: stream semantic registers (SSR) and a floating-point repetition instruction (FREP). SSRs allow the core to implicitly encode load/store instructions as register reads/writes, eliding many explicit memory instructions. The FREP extension decouples the floating-point and integer pipeline by sequencing instructions from a micro-loop buffer. These ISA extensions significantly reduce the pressure on the core and free it up for other tasks, making Snitch and FPU effectively dual-issue at a minimal incremental cost of 3.2 percent. The two low overhead ISA extensions make Snitch more flexible than a contemporary vector processor lane, achieving a $2\times$ 2 × energy-efficiency improvement. We have evaluated the proposed core and ISA extensions on an octa-core cluster in 22 nm technology. We achieve more than $6\times$ 6 × multi-core speed-up and a $3.5\times$ 3. 5 × gain in energy efficiency on several parallel microkernels. [ABSTRACT FROM AUTHOR]

Tight collaboration between manycore system designers and machine-learning experts is necessary to create a data-driven manycore design framework that integrates both learning and expert knowledge. Such a framework will be necessary to address the rising complexity of designing large-scale manycore systems and machine-learning techniques. [ABSTRACT FROM AUTHOR]

This article includes the following "mini-articles" about applications and examples of computing in astronomy: "Visualizing the Universe: Using Modern Graphics Cards to Understand the Physical World," by F. Alexander Bogert, Nicholas Smith, and John Holdener; "Visualizing Big Data in Astronomy: The Automated Movie Production Environment Distribution and Display (AMPED) Pipeline," by Eric M. De Jong; "Supporting Distributed, Collaborative Review and Classification of Fast Transient Events," by Andrew F. Hart, Luca Cinquini, Shakeh E. Khudikyan, David R. Thompson, Chris A. Mattmann, Kiri Wagstaff, Joseph Lazio, and Dayton L. Jones; "Big Data Technologies at JPL," by Dayton L. Jones; and "The Astrophysics Source Code Library: ascl.net," by Alice Allen, Lior Shamir, and Peter Teuben. [ABSTRACT FROM PUBLISHER]