论文标题
热 - 数据分析的分布式和GPU加速张量框架
HeAT -- a Distributed and GPU-accelerated Tensor Framework for Data Analytics
论文作者
论文摘要
为了应对可用数据的快速增长，数据分析和机器学习库的效率最近受到了越来越多的关注。尽管在传统的基于数组的计算中取得了巨大进步，但大多数人受单个计算节点上可用的资源的限制。因此，必须采取新颖的方法来利用分布式资源，例如分布式内存体系结构。为此，我们引入了HET，这是一种基于数组的数值编程框架，用于易于使用的Numpy样API，用于大规模并行处理。 Heat利用Pytorch作为节点 - 局部急切的执行引擎，并通过MPI在任意大型高性能计算系统上分配工作量。它既提供了低级阵列计算，也提供了各种高级算法。使用加热，Numpy用户可以充分利用其可用资源，从而大大降低分布式数据分析的障碍。与类似框架相比，热量达到了多达两个数量级的加速。
To cope with the rapid growth in available data, the efficiency of data analysis and machine learning libraries has recently received increased attention. Although great advancements have been made in traditional array-based computations, most are limited by the resources available on a single computation node. Consequently, novel approaches must be made to exploit distributed resources, e.g. distributed memory architectures. To this end, we introduce HeAT, an array-based numerical programming framework for large-scale parallel processing with an easy-to-use NumPy-like API. HeAT utilizes PyTorch as a node-local eager execution engine and distributes the workload on arbitrarily large high-performance computing systems via MPI. It provides both low-level array computations, as well as assorted higher-level algorithms. With HeAT, it is possible for a NumPy user to take full advantage of their available resources, significantly lowering the barrier to distributed data analysis. When compared to similar frameworks, HeAT achieves speedups of up to two orders of magnitude.
