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Abstracts

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Cosmological simulations present well-known difficulties scaling to large core counts because of the large spatial inhomogeneities and vast range of length scales induced by gravitational instability. These difficulties are compounded when baryonic physics is included which introduce their own multiscale challenges. In this talk I review efforts to scale the Enzo adaptive mesh refinement hydrodynamic cosmology code to O(100,000) cores, and I also discuss Cello--an extremely scalable AMR infrastructure under development at UCSD for the next generation of computer architectures which will underpin petascale Enzo.

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Eric Bohm, NCSA

Blue Waters: A Super-System to Explore the Expanse and Depth of 21st Century Science

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Scaling NAMD into the Petascale and Beyond

Many challenges arise when employing ever larger supercomputers for the simulation of biological molecules in the context of a mature molecular dynamics code.  Issues stemming from the scaling up of problem size, such as input and output require both parallelization and revisions to legacy file formats.  Order of magnitude increases in the number of processor cores evoke problems with O(P) structures, load balancing, and performance analysis.  New architectures present code optimization opportunities (VSX SIMD) which must be carefully applied to provide the desired performance improvements without dire costs in implementation time and code quality.  Looking beyond these imminent concerns for sustained petaflop performance on Blue Waters, we will also consider scalability concerns for future exascale machines.

Bill Kramer, NCSA

Blue Waters: A Super-System to Explore the Expanse and Depth of 21st Century Science

While many people think that Blue Waters means a single Power7 IH supercomputer, in reality, the Blue Waters Project is deploying an entire system architecture that includes an eco-system surrounding the Power7 IH system to make it highly effective, ultra-scale science and engineering. This is what we term the Blue Waters "Super System" which we will describe in detail in this talk along with its corresponding service architecture.

Ana Gainaru, UIUC/NCSA

Framework for

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Ana Gainaru, UIUC/NCSA

Framework for Event Log Analysis in HPC

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In a High Performance Computing infrastructure, it is particularly difficult to master the architecture as a whole. With the physical infrastructure, the platform management software and the users' applications, understanding the global behavior and diagnosing problems is quite challenging. And it is even more true in a petascale context with thousands of compute nodes to manage and a high occupation rate of the resources. A global study of the platform will thus consider the Resource and Job Management System (RJMS), the File System and the Applications triptych as a whole. Studying their behavior is complicated because it means having some knowledge of the applications requirements in terms of physical resources and access to the File System. In this presentation, we propose a first step toward an experimental approach that mix the use of Jobs Workloads patterns and File System access patterns that, once combined, will give a full set of jobs behaviors. These synthetic jobs will then be used to test and benchmark infrastructure, considering the RJMS and the File System.

Torsten Hoefler, NCSA

Application Performance Modeling on Petascale and Beyond

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Cache-coherent Non-Uniform Memory Access (ccNUMA) platforms based on multi-core chips are now a common resource in High Performance Computing. To overcome scalability issues in such platforms, the shared memory is physically distributed among several memory banks. Its memory access costs may vary depending on the distance between processing units and data. The main challenge of a ccNUMA platform is to manage efficiently threads, data distribution and communication over all the machine nodes. Charm++ is a parallel programming system that provides a portable programming model for platforms based on shared and distributed memory. In this work, we revisit some of the implementation decisions currently featured on Charm++ on the context of ccNUMA platforms. First, we studied the impact of the new -- shared-memory based -- inter-object communication scheme utilized by Charm+. We show how this shared-memory approach can impact the performance of Charm+ on ccNUMA machines. Second, we conduct a performance evaluation of the CPU and memory affinity mechanisms provided by Charm++ on ccNUMA platforms. Results show that SMP optimizations and affinity support can improve the overall performance of our benchmarks in up to 75%. Finally, in light of these studies, we have designed and implemented a NUMA-aware load balancing algorithm that addresses the issues found. The performance evaluation of our prototype showed results as good as the ones obtained by GreedyLB and significant improvements when compared to GreedyCommLBdistributed memory. In this work, we revisit some of the implementation decisions currently featured on Charm++ on the context of ccNUMA platforms. First, we studied the impact of the new -- shared-memory based -- inter-object communication scheme utilized by Charm+. We show how this shared-memory approach can impact the performance of Charm+ on ccNUMA machines. Second, we conduct a performance evaluation of the CPU and memory affinity mechanisms provided by Charm++ on ccNUMA platforms. Results show that SMP optimizations and affinity support can improve the overall performance of our benchmarks in up to 75%. Finally, in light of these studies, we have designed and implemented a NUMA-aware load balancing algorithm that addresses the issues found. The performance evaluation of our prototype showed results as good as the ones obtained by GreedyLB and significant improvements when compared to GreedyCommLB.

Thierry Gautier INRIA

On the cost of managing data flow dependencies for parallel programming.

Several parallel programming languages or libraries (TBB, Cilk+, OpenMP) allows to spawn independent tasks at runtime. In this talk, I will give an overview of the work about the Kaapi runtime system and its management of dependencies between tasks scheduled by a work stealing algorithm. I will show you that at a lower cost than TBB or Cilk+, it is possible to program with data flow dependencies.

Raymond Namyst INRIA/Univ. Bordeaux

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