An analytical approach to computing biomolecular electrostatic potential. II. Validation and applications

2014 14th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing

2014

Heterogeneity is increasing across all levels of computing, with the rise of accelerators such as GPUs, FPGAs, and other coprocessors into everything from cell phones to supercomputers. More quietly it is increasing with the rise of NUMA systems, hierarchical caching, OS noise, and a myriad of other factors. As heterogeneity becomes a fact of life, efficiently managing heterogeneous compute resources is becoming a critical, and ever more complex, task. The focus of this dissertation is to lay the foundation for an autonomic system for heterogeneous computing, employing runtime adaptation to improve performance portability and performance consistency while maintaining or increasing programmability. We investigate heterogeneity arising from a myriad of factors, grouped into the dimensions of locality and capability. This work has resulted in runtime schedulers capable of automatically detecting and mitigating heterogeneity in physically homogeneous systems through MPI and adaptive coscheduling for physically heterogeneous accelerator based systems as well as a synthesis of the two to address multiple levels of heterogeneity as a coherent whole. We also discuss our current work towards the next generation of fine-grained scheduling and synchronization across heterogeneous platforms in the design of a highly-scalable and portable concurrent queue for many-core systems. Each component addresses aspects of the urgent need for automated management of the extreme and ever expanding complexity introduced by heterogeneity. I have also had the good fortune to collaborate with both Lawrence Livermore National Laboratory and Argonne National Laboratory. Experiences as an intern at each lab has had a significant effect on the final shape of my dissertation. Special thanks go to Dr. Pavan Balaji (again), Dr. Bronis de Supinski (again) and Dr. Barry Rountree. These collaborations proved to be major turning points for me, both in my research and my life as a whole.

Section: Discussionmentioning

confidence: 99%

Runtime Adaptation for Autonomic Heterogeneous Computing

Scogland

2014 14th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing

2014

“…To evaluate our proof-of-concept, we have implemented OpenMP Accelerator directives versions of four applications, GEM [5], [6], [7], k-means, CG [8] and helmholtz which will be described in greater detail below. Each of the four presents a different pattern of execution, and different levels of fitness for GPU computing.…”

Section: Discussionmentioning

confidence: 99%

Heterogeneous Task Scheduling for Accelerated OpenMP

Scogland

Rountree

2012 IEEE 26th International Parallel and Distributed Processing Symposium

et al. 2012

Abstract-Heterogeneous systems with CPUs and computational accelerators such as GPUs, FPGAs or the upcoming Intel MIC are becoming mainstream. In these systems, peak performance includes the performance of not just the CPUs but also all available accelerators. In spite of this fact, the majority of programming models for heterogeneous computing focus on only one of these. With the development of Accelerated OpenMP for GPUs, both from PGI and Cray, we have a clear path to extend traditional OpenMP applications incrementally to use GPUs. The extensions are geared toward switching from CPU parallelism to GPU parallelism. However they do not preserve the former while adding the latter. Thus computational potential is wasted since either the CPU cores or the GPU cores are left idle. Our goal is to create a runtime system that can intelligently divide an accelerated OpenMP region across all available resources automatically. This paper presents our proof-of-concept runtime system for dynamic task scheduling across CPUs and GPUs. Further, we motivate the addition of this system into the proposed OpenMP for Accelerators standard. Finally, we show that this option can produce as much as a two-fold performance improvement over using either the CPU or GPU alone.

“…GEM is a molecular modeling application which allows one to visualize the electrostatic potential along the surface of a macromolecule [6]. GEM belongs to the 'N-Body' class of applications.…”

Section: Gemmentioning

confidence: 99%

“…In order to validate our performance prediction model, we choose the following three applications with known memory access patterns and partition camping effects: (1) GEM (Gaussian Electrostatic Model) [6], which is a molecular modeling application, (2) Clique-Counter, which is a graph analysis algorithm to count the number of cliques in large bi-directed graphs, and (3) Matrix Transpose. The GPU implementations of GEM [5] and Clique-Counter are part of our own prior and ongoing research, while the Matrix Step 1 Warp 1…”

Section: Application Suitementioning

confidence: 99%

Bounding the effect of partition camping in GPU kernels

Aji

Daga

Proceedings of the 8th ACM International Conference on Computing Frontiers

2011

Current GPU tools and performance models provide some common architectural insights that guide the programmers to write optimal code. We challenge and complement these performance models and tools, by modeling and analyzing a lesser known, but very severe performance pitfall, called Partition Camping, in NVIDIA GPUs. Partition Camping is caused by memory accesses that are skewed towards a subset of the available memory partitions, which may degrade the performance of GPU kernels by up to seven-fold. There is no existing tool that can detect the partition camping effect in GPU kernels.Unlike the traditional performance modeling approaches, we predict a performance range that bounds the partition camping effect in the GPU kernel. Our idea of predicting a performance range, instead of the exact performance, is more realistic due to the large performance variations induced by partition camping. We design and develop the prediction model by first characterizing the effects of partition camping with an indigenous suite of microbenchmarks. We then apply rigorous statistical regression techniques over the micro-benchmark data to predict the performance bounds of real GPU kernels, with and without the partition camping effect. We test the accuracy of our performance model by analyzing three real applications with known memory access patterns and partition camping effects. Our results show that the geometric mean of errors in our performance range prediction model is within 12% of the actual execution times.We also develop and present a very easy-to-use spreadsheet based tool called CampProf, which is a visual front-end to our performance range prediction model and can be used to gain insights into the degree of partition camping in GPU kernels. Lastly, we demonstrate how CampProf can be used to visually monitor the performance improvements in the kernels, as the partition camping effect is being removed.