3,003 research outputs found
Recommended from our members
Preparing sparse solvers for exascale computing.
Sparse solvers provide essential functionality for a wide variety of scientific applications. Highly parallel sparse solvers are essential for continuing advances in high-fidelity, multi-physics and multi-scale simulations, especially as we target exascale platforms. This paper describes the challenges, strategies and progress of the US Department of Energy Exascale Computing project towards providing sparse solvers for exascale computing platforms. We address the demands of systems with thousands of high-performance node devices where exposing concurrency, hiding latency and creating alternative algorithms become essential. The efforts described here are works in progress, highlighting current success and upcoming challenges. This article is part of a discussion meeting issue 'Numerical algorithms for high-performance computational science'
DAPHNE: An Open and Extensible System Infrastructure for Integrated Data Analysis Pipelines
Integrated data analysis (IDA) pipelines—that combine data management (DM) and query processing, high-performance computing
(HPC), and machine learning (ML) training and scoring—become
increasingly common in practice. Interestingly, systems of these
areas share many compilation and runtime techniques, and the
used—increasingly heterogeneous—hardware infrastructure converges as well. Yet, the programming paradigms, cluster resource
management, data formats and representations, as well as execution
strategies differ substantially. DAPHNE is an open and extensible
system infrastructure for such IDA pipelines, including language abstractions, compilation and runtime techniques, multi-level scheduling, hardware (HW) accelerators, and computational storage for
increasing productivity and eliminating unnecessary overheads. In
this paper, we make a case for IDA pipelines, describe the overall
DAPHNE system architecture, its key components, and the design
of a vectorized execution engine for computational storage, HW
accelerators, as well as local and distributed operations. Preliminary experiments that compare DAPHNE with MonetDB, Pandas,
DuckDB, and TensorFlow show promising results
- …