No abstract

Large scale graph analytics are an important class of problem in the modern data center. However, while data centers are trending towards a large number of heterogeneous processing nodes, graph analytics frameworks still operate under the assumption of uniform compute resources. In this paper, we develop heterogeneity-aware data ingress strategies for graph analytics workloads using the popular Power-Graph framework. We illustrate how simple estimates of relative node computational throughput can guide heterogeneityaware data partitioning algorithms to provide balanced graph cutting decisions. Our work enhances five online data ingress strategies from a variety of sources to optimize application execution for throughput differences in heterogeneous data centers. The proposed partitioning algorithms improve the runtime of several popular machine learning and data mining applications by as much as a 65% and on average by 32% as compared to the default, balanced partitioning approaches. CCS Concepts •Computer systems organization → Cloud computing; Heterogeneous (hybrid) systems; •Information systems → Data layout; •Computing methodologies → Distributed programming languages; •Mathematics of computing → Graph algorithms;

No abstract

No abstract

No abstract

No abstract

No abstract

Recently, GPGPUs have positioned themselves in the mainstream processor arena with their potential to perform a massive number of jobs in parallel. At the same time, many GPGPU benchmark suites have been proposed to evaluate the performance of GPGPUs. Both academia and industry have been introducing new sets of benchmarks each year while some already published benchmarks have been updated periodically. However, some benchmark suites contain benchmarks that are duplicates of each other or use the same underlying algorithm. This results in an excess of workloads in the same performance spectrum.In this paper, we provide a methodology to obtain a set of new GPGPU benchmarks that are located in the unexplored region of the performance spectrum. Our proposal uses statistical methods to understand the performance spectrum coverage and uniqueness of existing benchmark suites. Later we show techniques to identify areas that are not explored by existing benchmarks by visually showing the performance spectrum coverage. Finding unique key metrics for future benchmarks to broaden its performance spectrum coverage is also explored using hierarchical clustering and ranking by Hotelling's T 2 method. Finally, key metrics are categorized into GPGPU performance related components to show how future benchmarks can stress each of the categorized metrics to distinguish themselves in the performance spectrum. Our methodology can serve as a performance spectrum oriented guidebook for designing future GPGPU benchmarks.