论文标题
面向类型的Graph500基准测试
A Type-Oriented Graph500 Benchmark
论文作者
论文摘要
近年来，数据密集型工作负载已成为HPC的流行使用，以及可能不是HPC专家的数据科学家如何有效地对这些机器进行编程的问题。从简单的角度来看，使用诸如分区全球地址空间（PGA）之类的模型具有吸引力，但这些对程序员强加于程序的抽象会影响性能。我们提出了一种面向类型的编程方法，该方法是通过类型和类型系统编码的所有方面，该系统允许程序员编写简单的PGAS数据密集型HPC代码，然后，如果他们愿意，请通过修改类型信息来调整基本方面。本文认为在数据密集型工作负载中使用面向类型的编程以及PGAS内存模型的适用性。我们将面向类型的Graph500基准的实现与MPI参考实现相比，在可编程性和性能方面进行了比较，并评估如何在类型上定位其并行代码如何有助于数据密集型HPC字段。
Data intensive workloads have become a popular use of HPC in recent years and the question of how data scientists, who might not be HPC experts, can effectively program these machines is important to address. Whilst using models such as Partitioned Global Address Space (PGAS) is attractive from a simplicity point of view, the abstractions that these impose upon the programmer can impact performance. We propose an approach, type-oriented programming, where all aspects of parallelism are encoded via types and the type system which allows for the programmer to write simple PGAS data intensive HPC codes and then, if they so wish, tune the fundamental aspects by modifying type information. This paper considers the suitability of using type-oriented programming, with the PGAS memory model, in data intensive workloads. We compare a type-oriented implementation of the Graph500 benchmark against MPI reference implementations both in terms of programmability and performance, and evaluate how orienting their parallel codes around types can assist in the data intensive HPC field.
