Enabling and scaling biomolecular simulations of 100 million atoms on petascale machines with a multicore-optimized message-driven runtime

A 100-million-atom biomolecular simulation with NAMD is one of the three benchmarks for the NSF-funded sustainable petascale machine. Simulating this large molecular system on a petascale machine presents great challenges, including handling I/O, large memory footprint and getting good strong-scaling results. In this paper, we present parallel I/O techniques to enable the simula-tion. A new SMP model is designed to efficiently utilize ubiquitous wide multicore clusters by extending the CHARM++ asynchronous message-driven runtime. We exploit node-aware techniques to op-timize both the application and the underlying SMP runtime. Hi-erarchical load balancing is further exploited to scale NAMD to the full Jaguar PF Cray XT5 (224,076 cores) at Oak Ridge Na-tional Laboratory, both with and without PME full electrostatics, achieving 93 % parallel efficiency (vs 6720 cores) at 9 ms per step for a simple cutoff calculation. Excellent scaling is also obtained on 65,536 cores of the Intrepid Blue Gene/P at Argonne National Laboratory. 1

DAG-based software frameworks for PDEs

pre-printThe task-based approach to software and parallelism is well-known and has been proposed as a potential candidate, named the silver model, for exas-cale software. This approach is not yet widely used in the large-scale multi-core parallel computing of complex systems of partial differential equations. After surveying task-based approaches we investigate how well the Uintah software and an extension named Wasatch fit in the task-based paradigm and how well they perform on large scale parallel computers. The conclusion is that these approaches show great promise for petascale but that considerable algorithmic challenges remain

Exascale machines require new programming paradigms and runtimes

Extreme scale parallel computing systems will have tens of thousands of optionally accelerator-equiped nodes with hundreds of cores each, as well as deep memory hierarchies and complex interconnect topologies. Such Exascale systems will provide hardware parallelism at multiple levels and will be energy constrained. Their extreme scale and the rapidly deteriorating reliablity of their hardware components means that Exascale systems will exhibit low mean-time-between-failure values. Furthermore, existing programming models already require heroic programming and optimisation efforts to achieve high efficiency on current supercomputers. Invariably, these efforts are platform-specific and non-portable. In this paper we will explore the shortcomings of existing programming models and runtime systems for large scale computing systems. We then propose and discuss important features of programming paradigms and runtime system to deal with large scale computing systems with a special focus on data-intensive applications and resilience. Finally, we also discuss code sustainability issues and propose several software metrics that are of paramount importance for code development for large scale computing systems

Cactus Framework: Black Holes to Gamma Ray Bursts

Gamma Ray Bursts (GRBs) are intense narrowly-beamed flashes of gamma-rays of cosmological origin. They are among the most scientifically interesting astrophysical systems, and the riddle concerning their central engines and emission mechanisms is one of the most complex and challenging problems of astrophysics today. In this article we outline our petascale approach to the GRB problem and discuss the computational toolkits and numerical codes that are currently in use and that will be scaled up to run on emerging petaflop scale computing platforms in the near future. Petascale computing will require additional ingredients over conventional parallelism. We consider some of the challenges which will be caused by future petascale architectures, and discuss our plans for the future development of the Cactus framework and its applications to meet these challenges in order to profit from these new architectures

Towards Loosely-Coupled Programming on Petascale Systems

We have extended the Falkon lightweight task execution framework to make loosely coupled programming on petascale systems a practical and useful programming model. This work studies and measures the performance factors involved in applying this approach to enable the use of petascale systems by a broader user community, and with greater ease. Our work enables the execution of highly parallel computations composed of loosely coupled serial jobs with no modifications to the respective applications. This approach allows a new-and potentially far larger-class of applications to leverage petascale systems, such as the IBM Blue Gene/P supercomputer. We present the challenges of I/O performance encountered in making this model practical, and show results using both microbenchmarks and real applications from two domains: economic energy modeling and molecular dynamics. Our benchmarks show that we can scale up to 160K processor-cores with high efficiency, and can achieve sustained execution rates of thousands of tasks per second.Comment: IEEE/ACM International Conference for High Performance Computing, Networking, Storage and Analysis (SuperComputing/SC) 200