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The GraphBLAS C specification provisional release 1.0 is complete. To manage the scope of the project, we had to defer important functionality to a future version of the specification. For example, we are well aware that many algorithms benefit from an inspector-executor execution strategy. We also know that users would benefit from a number of standard predefined semirings as well as more general...
Accelerators are specialized hardware designs that generally guarantee two to three orders of magnitude higher energy efficiency than general-purpose processor cores for their target computational kernels. To cope with the complexity of integrating many accelerators into heterogeneous systems, we have proposed Embedded Scalable Platforms (ESP) that combines a flexible architecture with a companion...
Deep Neural Networks (DNNs) have emerged as a core tool for machine learning. The computations performed during DNN training and inference are dominated by operations on the weight matrices describing the DNN. As DNNs incorporate more stages and more nodes per stage, these weight matrices may be required to be sparse because of memory limitations. The GraphBLAS.org math library standard was developed...
The numerical treatment of variational problems gives rise to large sparse matrices, which are typically assembled by coalescing elementary contributions. As the explicit matrix form is required by numerical solvers, the assembly step can be a potential bottleneck, especially in implicit and time dependent settings where considerable updates are needed. On standard HPC platforms, this process can...
Increasing amounts of data from varied sources, particularly in the fields of machine learning and graph analytics, are causing storage requirements to grow rapidly. A variety of technologies exist for storing and sharing these data, ranging from parallel file systems used by supercomputers to distributed block storage systems found in clouds. Relatively few comparative measurements exist to inform...
This paper introduces xDCI, a Data Science Cyber-infrastructure to support research in a number of scientific domains including genomics, environmental science, biomedical and health science, and social science. xDCI leverages open-source software packages such as the integrated Rule Oriented Data System and the CyVerse Discovery Environment to address significant challenges in data storage, sharing,...
With NVIDA Tegra Jetson X1 and Pascal P100 GPUs, NVIDIA introduced hardware-based computation on FP16 numbers also called half-precision arithmetic. In this talk, we will introduce the steps required to build a viable benchmark for this new arithmetic format. This will include the connections to established IEEE floating point standards and existing HPC benchmarks. The discussion will focus on performance...
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