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

Adapt or Become Extinct!:The Case for a Unified Framework for Deployment-Time Optimization

The High-Performance Computing ecosystem consists of a large variety of execution platforms that demonstrate a wide diversity in hardware characteristics such as CPU architecture, memory organization, interconnection network, accelerators, etc. This environment also presents a number of hard boundaries (walls) for applications which limit software development (parallel programming wall), performance (memory wall, communication wall) and viability (power wall). The only way to survive in such a demanding environment is by adaptation. In this paper we discuss how dynamic information collected during the execution of an application can be utilized to adapt the execution context and may lead to performance gains beyond those provided by static information and compile-time adaptation. We consider specialization based on dynamic information like user input, architectural characteristics such as the memory hierarchy organization, and the execution profile of the application as obtained from the execution platform\u27s performance monitoring units. One of the challenges of future execution platforms is to allow the seamless integration of these various kinds of information with information obtained from static analysis (either during ahead-of-time or just-in-time) compilation. We extend the notion of information-driven adaptation and outline the architecture of an infrastructure designed to enable information flow and adaptation through-out the life-cycle of an application

Applications, tools and techniques on the road to exascale computing

This volume of the book series “Advances in Parallel Computing” contains the proceedings of ParCo2011, the 14th biennial ParCo Conference, held from 31 August to 3 September 2011, in Ghent, Belgium. In an era when physical limitations have slowed down advances in the performance of single processing units, and new scientific challenges require exascale speed, parallel processing has gained momentum as a key gateway to HPC (High Performance Computing). Historically, the ParCo conferences have focused on three main themes: Algorithms, Architectures (both hardware and software) and Applications. Nowadays, the scenery has changed from traditional multiprocessor topologies to heterogeneous manycores, incorporating standard CPUs, GPUs (Graphics Processing Units) and FPGAs (Field Programmable Gate Arrays). These platforms are, at a higher abstraction level, integrated in clusters, grids, and clouds. This is reflected in the papers presented at the conference and the contributions as included in these proceedings. An increasing number of new algorithms are optimized for heterogeneous platforms and performance tuning is targeting extreme scale computing. Heterogeneous platforms utilising the compute power and energy efficiency of GPGPUs (General Purpose GPUs) are clearly becoming mainstream HPC systems for a large number of applications in a wide spectrum of application areas. These systems excel in areas such as complex system simulation, real-time image processing and visualisation, etc. High performance computing accelerators may well become the cornerstone of exascale computing applications such as 3-D turbulent combustion flows, nuclear energy simulations, brain research, financial and geophysical modelling. The exploration of new architectures, programming tools and techniques was evidenced by the mini-symposia “Parallel Computing with FPGAs” and “Exascale Programming Models”. The need for exascale hardware and software was also stressed in the industrial session, with contributions from Cray and the European exascale software initiative. Our sincere appreciation goes to the keynote speakers who gave their perspectives on the impact of parallel computing today and the road to exascale computing tomorrow. Our heartfelt thanks go to the authors for their valuable scientific contributions and to the programme committee who reviewed the papers and provided constructive remarks. The international audience was inspired by the quality of the presentations. The attendance and interaction was high and the conference has been an agora where many fruitful ideas were exchanged and explored. We wish to express our sincere thanks to the organizers for the smooth operation of the conference. The University conference centre Het Pand offered an excellent environment for the conference as it allowed delegates to interact informally and easily. A special word of thanks is due to the management and support staff of Het Pand for their proficient and friendly support. The organizers managed to put together an extensive social programme. This included a reception at the medieval Town Hall of Ghent as well as a memorable conference dinner. These social events stimulated interaction amongst delegates and resulted in many new contacts being made. Finally we wish to thank all the many supporters who assisted in the organization and successful running of the event. Erik D'Hollander, Ghent University, Belgium Koen De Bosschere, Ghent University, Belgium Gerhard R. Joubert, TU Clausthal, Germany David Padua, University of Illinois, USA Frans Peters, Philips Research, Netherland

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

Using Cognitive Computing for Learning Parallel Programming: An IBM Watson Solution

While modern parallel computing systems provide high performance resources, utilizing them to the highest extent requires advanced programming expertise. Programming for parallel computing systems is much more difficult than programming for sequential systems. OpenMP is an extension of C++ programming language that enables to express parallelism using compiler directives. While OpenMP alleviates parallel programming by reducing the lines of code that the programmer needs to write, deciding how and when to use these compiler directives is up to the programmer. Novice programmers may make mistakes that may lead to performance degradation or unexpected program behavior. Cognitive computing has shown impressive results in various domains, such as health or marketing. In this paper, we describe the use of IBM Watson cognitive system for education of novice parallel programmers. Using the dialogue service of the IBM Watson we have developed a solution that assists the programmer in avoiding common OpenMP mistakes. To evaluate our approach we have conducted a survey with a number of novice parallel programmers at the Linnaeus University, and obtained encouraging results with respect to usefulness of our approach