基于GPU的多网格层次科学数据重构加速
Accelerating Multigrid-based Hierarchical Scientific Data Refactoring on GPUs
摘要：
科学数据的快速增长以及计算速度和I/O带宽之间的差距越来越大，使得存储和共享科学模拟产生的所有数据变得越来越不可行。相反，我们需要减少数据量的方法：理想情况下，可以自适应地扩展数据量的方法，以便在不同情况下进行性能和保真度权衡的协商。基于多重网格的分层数据表示有望解决这个问题，允许不同分辨率之间的灵活转换，例如，数据可以以高保真度创建，然后通过逻辑简单和数学上合理的操作以较低的保真度传输或存储。然而，直到现在，创建、访问、减少和以其他方式操作这些表示的相对高的成本阻碍了这些表示的有效使用。我们在这里描述了用于GPU加速器的高度优化的数据重构内核，这些内核能够以基于多网格的分层形式有效地创建和操作数据。我们证明，我们的优化设计可以实现高达250 TB/s的聚合数据重构吞吐量-理论峰值的83%-在1024个节点的首脑会议超级计算机。我们展示了我们的优化设计，将其应用到一个大规模的科学可视化工作流程和MGARD有损压缩软件。
英文摘要：
Rapid growth in scientific data and a widening gap between computational speed and I/O bandwidth make it increasingly infeasible to store and share all data produced by scientific simulations. Instead, we need methods for reducing data volumes: ideally, methods that can scale data volumes adaptively so as to enable negotiation of performance and fidelity tradeoffs in different situations. Multigrid-based hierarchical data representations hold promise as a solution to this problem, allowing for flexible conversion between different fidelities so that, for example, data can be created at high fidelity and then transferred or stored at lower fidelity via logically simple and mathematically sound operations. However, the effective use of such representations has been hindered until now by the relatively high costs of creating, accessing, reducing, and otherwise operating on such representations. We describe here highly optimized data refactoring kernels for GPU accelerators that enable efficient creation and manipulation of data in multigrid-based hierarchical forms. We demonstrate that our optimized design can achieve up to 250 TB/s aggregated data refactoring throughput -- 83% of theoretical peak -- on 1024 nodes of the Summit supercomputer. We showcase our optimized design by applying it to a large-scale scientific visualization workflow and the MGARD lossy compression software.
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