Three parallel algorithms for classical molecular dynamics are presented. The first assigns each processor a fixed subset of atoms; the second assigns each a fixed subset of inter-atomic forces to compute; the third assigns each a fixed spatial region. The algorithms are suitable for molecular dynamics models which can be difficult to parallelize efficiently—those with short-range forces where the neighbors of each atom change rapidly. They can be implemented on any distributed-memory parallel machine which allows for message-passing of data between independently executing processors. The algorithms are tested on a standard Lennard-Jones benchmark problem for system sizes ranging from 500 to 100,000,000 atoms on several parallel supercomputers--the nCUBE 2, Intel iPSC/860 and Paragon, and Cray T3D. Comparing the results to the fastest reported vectorized Cray Y-MP and C90 algorithm shows that the current generation of parallel machines is competitive with conventional vector supercomputers even for small problems. For large problems, the spatial algorithm achieves parallel efficiencies of 90% and a 1840-node Intel Paragon performs up to 165 faster than a single Cray C9O processor. Trade-offs between the three algorithms and guidelines for adapting them to more complex molecular dynamics simulations are also discussed.

Fast parallel algorithms for short-range molecular dynamics

Several studies in Bioinformatics, Computational Biology and Systems Biology rely on the definition of physico-chemical or mathematical models of biological systems at different scales and levels of complexity, ranging from the interaction of atoms in single molecules up to genome-wide interaction networks. Traditional computational methods and software tools developed in these research fields share a common trait: they can be computationally demanding on Central Processing Units (CPUs), therefore limiting their applicability in many circumstances. To overcome this issue, general-purpose Graphics Processing Units (GPUs) are gaining an increasing attention by the scientific community, as they can considerably reduce the running time required by standard CPU-based software, and allow more intensive investigations of biological systems. In this review, we present a collection of GPU tools recently developed to perform computational analyses in life science disciplines, emphasizing the advantages and the drawbacks in the use of these parallel architectures. The complete list of GPU-powered tools here reviewed is available at http://bit.ly/gputools.

/pdf/graphics-processing-units-in-bioinformatics-computational-2m7e2cgkl8.pdf

Graphics processing units in bioinformatics, computational biology and systems biology.

We examine the I/O behavior of thousands of supercomputing applications "in the wild," by analyzing the Darshan logs of over a million jobs representing a combined total of six years of I/O behavior across three leading high-performance computing platforms We mined these logs to analyze the I/O behavior of applications across all their runs on a platform; the evolution of an application's I/O behavior across time, and across platforms; and the I/O behavior of a platform's entire workload Our analysis techniques can help developers and platform owners improve I/O performance and I/O system utilization, by quickly identifying underperforming applications and offering early intervention to save system resources We summarize our observations regarding how jobs perform I/O and the throughput they attain in practice

https://dl.acm.org/doi/pdf/10.1145/2749246.2749269

A Multiplatform Study of I/O Behavior on Petascale Supercomputers

Cloud computing has gained much popularity recently, and many companies now offer a variety of public cloud computing services, such as Google Ap-pEngine, Amazon AWS, and Microsoft Azure. These services differ in service models and pricing schemes, making it challenging for customers to choose the best suited cloud provider for their applications. This paper proposes a framework called CloudCmp to help a customer select a cloud provider. We outline the design of CloudCmp and highlight the main technical challenges. CloudCmp includes a set of benchmarking tools that compare the common services offered by cloud providers, and uses the benchmarking results to predict the performance and costs of a customer's application when deployed on a cloud provider. We present preliminary benchmarking results on three representative cloud providers. These results show that the performance and costs of various cloud providers differ significantly, suggesting that CloudCmp, if implemented, will have practical relevance.

/pdf/cloudcmp-shopping-for-a-cloud-made-easy-2pi5n3rkuv.pdf

CloudCmp: shopping for a cloud made easy

Drug screening is an important part of the drug development pipeline for the pharmaceutical industry. Traditional, lab-based methods are increasingly being augmented with computational methods, ranging from simple molecular similarity searches through more complex pharmacophore matching to more computationally intensive approaches, such as molecular docking. The latter simulates the binding of drug molecules to their targets, typically protein molecules. In this work, we describe BUDE, the Bristol University Docking Engine, which has been ported to the OpenCL industry standard parallel programming language in order to exploit the performance of modern many-core processors. Our highly optimized OpenCL implementation of BUDE sustains 1.43 TFLOP/s on a single Nvidia GTX 680 GPU, or 46% of peak performance. BUDE also exploits OpenCL to deliver effective performance portability across a broad spectrum of different computer architectures from different vendors, including GPUs from Nvidia and AMD, Intel's Xeon Phi and multi-core CPUs with SIMD instruction sets.

/pdf/high-performance-in-silico-virtual-drug-screening-on-many-57u3nq94vy.pdf

High performance in silico virtual drug screening on many-core processors

Hardware accelerators such as GPGPUs are becoming increasingly common in HPC platforms and their use is widely recognised as being one of the most promising approaches for reaching exascale levels of performance. Large HPC centres, such as AWE, have made huge investments in maintaining their existing scientific software codebases, the vast majority of which were not designed to effectively utilise accelerator devices. Consequently, HPC centres will have to decide how to develop their existing applications to take best advantage of future HPC system architectures. Given limited development and financial resources, it is unlikely that all potential approaches will be evaluated for each application. We are interested in how this decision making can be improved, and this work seeks to directly evaluate three candidate technologies-OpenACC, OpenCL and CUDA-in terms of performance, programmer productivity, and portability using a recently developed Lagrangian-Eulerian explicit hydrodynamics mini-application. We find that OpenACC is an extremely viable programming model for accelerator devices, improving programmer productivity and achieving better performance than OpenCL and CUDA.

Accelerating Hydrocodes with OpenACC, OpeCL and CUDA

An investigation of the performance portability of OpenCL

There are a number of challenges facing the High Performance Computing (HPC) community, including increasing levels of concurrency (threads, cores, nodes), deeper and more complex memory hierarchies (register, cache, disk, network), mixed hardware sets (CPUs and GPUs) and increasing scale (tens or hundreds of thousands of processing elements). Assessing the performance of complex scientific applications on specialised high-performance computing architectures is difficult. In many cases, traditional computer benchmarking is insufficient as it typically requires access to physical machines of equivalent (or similar) specification and rarely relates to the potential capability of an application. A technique known as application performance modelling addresses many of these additional requirements. Modelling allows future architectures and/or applications to be explored in a mathematical or simulated setting, thus enabling hypothetical questions relating to the configuration of a potential future architecture to be assessed in terms of its impact on key scientific codes.

This paper describes the Warwick Performance Prediction (WARPP) simulator, which is used to construct application performance models for complex industry-strength parallel scientific codes executing on thousands of processing cores. The capability and accuracy of the simulator is demonstrated through its application to a scientific benchmark developed by the United Kingdom Atomic Weapons Establishment (AWE). The results of the simulations are validated for two different HPC architectures, each case demonstrating a greater than 90% accuracy for run-time prediction. Simulation results, collected from runs on a standard PC, are provided for up to 65,000 processor cores. It is also shown how the addition of operating system jitter to the simulator can improve the quality of the application performance model results.

/pdf/warpp-a-toolkit-for-simulating-high-performance-parallel-3bww7uxxk9.pdf

WARPP: a toolkit for simulating high-performance parallel scientific codes

In this work we directly evaluate five candidate programming models for future exascale applications (MPI, MPI+OpenMP, MPI+OpenACC, MPI+CUDA and CAF) using a recently developed Lagrangian-Eulerian explicit hydrodynamics mini-application. The aim of this work is to better inform the exacsale planning at large HPC centres such as AWE. Such organisations invest significant resources maintaining and updating existing scientific codebases, many of which were not designed to run at the scale required to reach exascale levels of computation on future system architectures. We present our results and experiences of scaling these different approaches to high node counts on existing large-scale Cray systems (Titan and HECToR). We also examine the effect that improving the mapping between process layout and the underlying machine interconnect topology can have on performance and scalability, as well as highlighting several communication-focused optimisations.

/pdf/cloverleaf-preparing-hydrodynamics-codes-for-exascale-4p6sox58yc.pdf

CloverLeaf: Preparing Hydrodynamics Codes for Exascale

Input/Output (I/O) operations can represent a significant proportion of the run-time of parallel scientific computing applications. Although there have been several advances in file format libraries, file system design and I/O hardware, a growing divergence exists between the performance of parallel file systems and the compute clusters that they support. In this paper, we document the design and application of the RIOT I/O toolkit (RIOT) being developed at the University of Warwick with our industrial partners at the Atomic Weapons Establishment and Sandia National Laboratories. We use the toolkit to assess the performance of three industry-standard I/O benchmarks on three contrasting supercomputers, ranging from a mid-sized commodity cluster to a large-scale proprietary IBM BlueGene/P system. RIOT provides a powerful framework in which to analyse I/O and parallel file system behaviour—we demonstrate, for example, the large file locking overhead of IBM's General Parallel File System, which can consume nearly 30% of the total write time in the FLASH-IO benchmark. Through I/O trace analysis, we also assess the performance of HDF-5 in its default configuration, identifying a bottleneck created by the use of suboptimal Message Passing Interface hints. Furthermore, we investigate the performance gains attributed to the Parallel Log-structured File System (PLFS) being developed by EMC Corporation and the Los Alamos National Laboratory. Our evaluation of PLFS involves two high-performance computing systems with contrasting I/O backplanes and illustrates the varied improvements to I/O that result from the deployment of PLFS (ranging from up to 25× speed-up in I/O performance on a large I/O installation to 2× speed-up on the much smaller installation at the University of Warwick).

/pdf/parallel-file-system-analysis-through-application-i-o-5eojo4n9ff.pdf

J. A. Herdman

Papers

Accelerating Hydrocodes with OpenACC, OpeCL and CUDA

An investigation of the performance portability of OpenCL

WARPP: a toolkit for simulating high-performance parallel scientific codes

CloverLeaf: Preparing Hydrodynamics Codes for Exascale

Parallel File System Analysis Through Application I/O Tracing