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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'
DINOMO: An Elastic, Scalable, High-Performance Key-Value Store for Disaggregated Persistent Memory (Extended Version)
We present Dinomo, a novel key-value store for disaggregated persistent
memory (DPM). Dinomo is the first key-value store for DPM that simultaneously
achieves high common-case performance, scalability, and lightweight online
reconfiguration. We observe that previously proposed key-value stores for DPM
had architectural limitations that prevent them from achieving all three goals
simultaneously. Dinomo uses a novel combination of techniques such as ownership
partitioning, disaggregated adaptive caching, selective replication, and
lock-free and log-free indexing to achieve these goals. Compared to a
state-of-the-art DPM key-value store, Dinomo achieves at least 3.8x better
throughput on various workloads at scale and higher scalability, while
providing fast reconfiguration.Comment: This is an extended version of the full paper to appear in PVLDB
15.13 (VLDB 2023
Uintah parallelism infrastructure: a performance evaluation on the SGI origin 2000
ManuscriptUintah is a component-based visual problem solving environment (PSE) designed to specifically address the unique problems inherent in running massively parallel scientific computations on terascale computing platforms. In particular, development of the Uintah system is part of the C-SAFE [2] effort to study the interactions between hydrocarbon fires, structures and high-energy materials (explosives and propellants). In this paper we describe methods for generating meaningful performance measurements for the Uintah PSE runing on the SGI Origin 2000 multiprocessor architecture (these methods are applicable to many other applications.) These techniques include utilizing the non-intrusive performance counters built into the R10k and R12k processors, controlling process placement, controlling memory layout, and utilization of a task graph approach to specifying and solving the problem
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