EAGER: Developing scalable benchmark mini-apps for graph engine comparison

EAGER：开发可扩展的基准迷你应用程序以进行图形引擎比较

基本信息

批准号：
1642280
负责人：
Peter Kogge
金额：
$ 29.99万
依托单位：
University of Notre Dame
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2016
资助国家：
美国
起止时间：
2016-08-01 至 2019-07-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1642280&HistoricalAwards=false
关键词：
EAGER Developing scalable benchmark mini

项目摘要

The last decade has seen the growth of extremely large, unstructured, and dynamic data sets, loosely termed Big Data. However, there is a growing desire to extract not just specific properties of collections of such facts, but also relationships between the underlying entities in that data. Examples come from a broad swatch of modern life: bioinformatics, financial, recommendation systems, cyber and national security, and social networks. Graphs have emerged as a valuable and productive paradigm for expressing such problems, where a graph is a collection of a set of objects (vertices) where some pairs of objects are connected by links (edges) that represent some relation between the two. In the last decade there has been an explosion in support for graphs, with widely differing execution models and targeted applicability. Although numerous graph benchmarks have been proposed, only one has had a rigorous accumulation of performance data from multiple platforms (www.graph500.org). Computation is over a whole static graph, whereas the real world sees applications where update data is streaming into large persistent graphs, and very many small targeted queries may be in progress at once.Given the expected productivity increase of using a graph programming paradigm over conventional programming, especially for parallel systems, it is of growing importance to have common mini-apps that can be used for cross-paradigm comparisons. Also, given the continued increase in graph sizes, it is important to understand how the underlying graph engines scale both in the size and type of the target graphs and in the amount and mix of parallelism and concurrency they can support.This project addresses this need. In collaboration with commercial and government research labs, the primary objective is on defining a set of mini-apps that reflect complex real-world applications more sophisticated than today's simple benchmarks, converting these mini-apps to the existing major graph packages, and then running them on a wide range of parallel systems. The wider impact can be significant. Identification of relevant mini-apps and how they perform across different systems will provide insight into both how to write more complete graph applications in more scalable ways, and which aspects of which programming systems and platforms are best suited. It is also expected that not all mini-apps will be expressible in all the current paradigms, providing insight to the developers of those paradigms on expressibility issues. Given the relative infancy of such graph packages such insight now can radically improve their applicability to real applications in the future.

在过去的十年中，极大，非结构化和动态数据集的增长宽松地称为大数据。但是，不仅希望提取此类事实的集合的特定特性，还越来越多地提取该数据中基础实体之间的关系。例子来自现代生活的广泛色彩：生物信息学，财务，推荐系统，网络和国家安全以及社交网络。图表已成为表达此类问题的有价值且富有成效的范式，其中图是一组对象（顶点）的集合，其中一对对象通过链接（边缘）连接，代表两者之间的某些关系。在过去的十年中，对图形的支持爆炸了，其执行模型差异很大和针对性的适用性。尽管已经提出了许多图基准测试，但只有一个来自多个平台（www.graph500.org）的性能数据的严格积累。计算在整个静态图表上，而现实世界中看到更新数据流入大型持久图的应用，并且许多小的目标查询可能会立即进行。赋予预期的生产率提高，使用图形编程范式而不是常规编程，尤其是对平行系统的重要性，它具有相当的Mini-Mini-Mini-apps，可用于比较型号。同样，鉴于图形尺寸的持续增加，重要的是要了解基础图引擎如何在目标图的大小和类型以及可以支持的并发和并发的数量和混合中缩放如何缩放。与商业和政府研究实验室合作，主要目标是定义一组小型应用程序，这些小型应用反映了比当今简单的简单基准更复杂的复杂现实世界应用程序，将这些微型应用程序转换为现有的主要图形包装，然后在各种并行系统上运行它们。更广泛的影响可能很大。识别相关的迷你应用程序及其在不同系统中的执行方式，将提供有关如何以更可扩展的方式编写更完整的图形应用程序的洞察力，以及最适合哪些编程系统和平台的方面。还可以预期，并非所有的迷你应用都可以在当前的所有范式中表现出来，从而为这些范式的开发商提供有关表达性问题的见解。鉴于此类图形包的相对起步，现在这种洞察力可以从根本上提高其对实际应用的适用性。

项目成果

期刊论文数量（6）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Graph Analytics: Complexity, Scalability, and Architectures

图分析：复杂性、可扩展性和架构

DOI：
发表时间：
2017
期刊：
HPBDC Workshop at Int. Parallel and Dist. Processing Conf.
影响因子：
0
作者：
Kogge, Peter M.
通讯作者：
Kogge, Peter M.

Scalability of Hybrid Sparse Matrix Dense Vector (SpMV) Multiplication

DOI：
10.1109/hpcs.2018.00072
发表时间：
2018-07
期刊：
2018 International Conference on High Performance Computing & Simulation (HPCS)
影响因子：
0
作者：
Brian A. Page;P. Kogge
通讯作者：
Brian A. Page;P. Kogge

Optimizing for KNL Usage Modes When Data Doesn’t Fit in MCDRAM

当数据不适合 MCDRAM 时优化 KNL 使用模式

DOI：
10.1145/3225058.3225116
发表时间：
2018
期刊：
International Conference on Parallel Processing
影响因子：
0
作者：
Butcher, Neil;Olivier, Stephen L.;Berry, Jonathan;Hammond, Simon D.;Kogge, Peter M.
通讯作者：
Kogge, Peter M.

Introducing Streaming into Linear Algebra-based Sparse Graph Algorithms

将流引入基于线性代数的稀疏图算法

DOI：
发表时间：
2019
期刊：
International Conference on High Performance Computing & Simulation
影响因子：
0
作者：
Kogge, Peter M.;Butcher, Neil A.;Page, Brian A.
通讯作者：
Page, Brian A.

Scalability of Hybrid SpMV on Intel Xeon Phi Knights Landing

DOI：
10.1109/hpcs48598.2019.9188154
发表时间：
2019-07
期刊：
2019 International Conference on High Performance Computing & Simulation (HPCS)
影响因子：
0
作者：
Brian A. Page;P. Kogge
通讯作者：
Brian A. Page;P. Kogge