Though the performance of many scientific codes is dominated by memory behavior, our ability to describe, capture, compare, and recreate that behavior is quite limited. This inability underlies much of the complexity in the field of performance analysis: it is fundamentally difficult to relate benchmarks and applications or use realistic workloads to guide system design and procurement. An observable, reproducible, and machine-independent memory characterization is needed.

Using a large HPC platform, we investigate the effectiveness of "symbiotic space-sharing", a technique that improves system throughput by executing parallel applications in combinations and configurations that alleviate pressure on shared resources. We demonstrate that relevant benchmarks commonly suffer a 10-60% penalty in runtime efficiency due to memory resource bottlenecks and up to several orders of magnitude for I/O. We show that this penalty can be often mitigated, and sometimes virtually eliminated, by symbiotic space-sharing techniques and deploy a prototype scheduler that leverages these findings to improve system throughput by 20%

Symbiotic space-sharing is a technique that can improve system throughput by executing parallel applications in combinations and configurations that alleviate pressure on shared resources. We have shown prototype schedulers that leverage such techniques to improve throughput by 20% over conventional space-sharing schedulers when resource bottlenecks are known. Such evaluations have utilized benchmark workloads and proposed that schedulers be informed of resource bottlenecks by users at job submission time; in this work, we investigate the accuracy with which users can actually identify resource bottlenecks in real applications and the implications of these predictions for symbiotic space-sharing of production workloads. Using a large HPC platform, a representative application workload, and a sampling of expert users, we show that user inputs are of value and that for our chosen workload, user-guided symbiotic scheduling can improve throughput over conventional space-sharing by 15-22%.

Several benchmarks for measuring the memory performance of HPC systems along dimensions of spatial and temporal memory locality have recently been proposed. However, little is understood about the relationships of these benchmarks to real applications and to each other. We propose a methodology for producing architecture-neutral characterizations of the spatial and temporal locality exhibited by the memory access patterns of applications. We demonstrate that the results track intuitive notions of locality on several synthetic and application benchmarks. We employ the methodology to analyze the memory performance components of the HPC Challenge Benchmarks, the Apex-MAP benchmark, and their relationships to each other and other benchmarks and applications. We show that this analysis can be used to both increase understanding of the benchmarks and enhance their usefulness by mapping them, along with applications, to a 2-D space along axes of spatial and temporal locality.