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

Transcriptomics studies often rely on partial reference transcriptomes that fail to capture the full catalog of transcripts and their variations. Recent advances in sequencing technologies and assembly algorithms have facilitated the reconstruction of the entire transcriptome by deep RNA sequencing (RNA-seq), even without a reference genome. However, transcriptome assembly from billions of RNA-seq reads, which are often very short, poses a significant informatics challenge. This Review summarizes the recent developments in transcriptome assembly approaches - reference-based, de novo and combined strategies-along with some perspectives on transcriptome assembly in the near future.

/pdf/next-generation-transcriptome-assembly-ns6cyvpfx6.pdf

Next-generation transcriptome assembly

This review discusses the many roles atomistic computer simulations of macromolecular (for example, protein) receptors and their associated small-molecule ligands can play in drug discovery, including the identification of cryptic or allosteric binding sites, the enhancement of traditional virtual-screening methodologies, and the direct prediction of small-molecule binding energies. The limitations of current simulation methodologies, including the high computational costs and approximations of molecular forces required, are also discussed. With constant improvements in both computer power and algorithm design, the future of computer-aided drug design is promising; molecular dynamics simulations are likely to play an increasingly important role.

/pdf/molecular-dynamics-simulations-and-drug-discovery-1wu2ajeo71.pdf

Molecular dynamics simulations and drug discovery

We design and implement Mars, a MapReduce framework, on graphics processors (GPUs). MapReduce is a distributed programming framework originally proposed by Google for the ease of development of web search applications on a large number of commodity CPUs. Compared with CPUs, GPUs have an order of magnitude higher computation power and memory bandwidth, but are harder to program since their architectures are designed as a special-purpose co-processor and their programming interfaces are typically for graphics applications. As the first attempt to harness GPU's power for MapReduce, we developed Mars on an NVIDIA G80 GPU, which contains over one hundred processors, and evaluated it in comparison with Phoenix, the state-of-the-art MapReduce framework on multi-core CPUs. Mars hides the programming complexity of the GPU behind the simple and familiar MapReduce interface. It is up to 16 times faster than its CPU-based counterpart for six common web applications on a quad-core machine.

/pdf/mars-a-mapreduce-framework-on-graphics-processors-kx8ld27s5q.pdf

Mars: a MapReduce framework on graphics processors

We demonstrate that a video gaming machine containing two consumer graphical cards can outpace a state-of-the-art quad-core processor workstation by a factor of more than 180× in Hartree−Fock energy + gradient calculations. Such performance makes it possible to run large scale Hartree−Fock and Density Functional Theory calculations, which typically require hundreds of traditional processor cores, on a single workstation. Benchmark Born−Oppenheimer molecular dynamics simulations are performed on two molecular systems using the 3-21G basis set - a hydronium ion solvated by 30 waters (94 atoms, 405 basis functions) and an aspartic acid molecule solvated by 147 waters (457 atoms, 2014 basis functions). Our GPU implementation can perform 27 ps/day and 0.7 ps/day of ab initio molecular dynamics simulation on a single desktop computer for these systems.

Quantum Chemistry on Graphical Processing Units. 3. Analytical Energy Gradients, Geometry Optimization, and First Principles Molecular Dynamics.

Sequence alignment is a common and often repeated task in molecular biology. Typical alignment operations consist of finding similarities between a pair of sequences (pairwise sequence alignment) or a family of sequences (multiple sequence alignment). The need for speeding up this treatment comes from the rapid growth rate of biological sequence databases: every year their size increases by a factor of 1.5 to 2. In this paper, we present a new approach to high-performance biological sequence alignment based on commodity PC graphics hardware. Using modern graphics processing units (GPUs) for high-performance computing is facilitated by their enhanced programmability and motivated by their attractive price/performance ratio and incredible growth in speed. To derive an efficient mapping onto this type of architecture, we have reformulated dynamic-programming-based alignment algorithms as streaming algorithms in terms of computer graphics primitives. Our experimental results show that the GPU-based approach allows speedups of more than one order of magnitude with respect to optimized CPU implementations.

Streaming Algorithms for Biological Sequence Alignment on GPUs

https://twiki.di.uniroma1.it/pub/CI/WebHome/MDCuda08.pdf

Accelerating molecular dynamics simulations using Graphics Processing Units with CUDA

Protein sequences with unknown functionality are often compared to a set of known sequences to detect functional similarities. Efficient dynamic programming algorithms exist for this problem, however current solutions still require significant scan times. These scan time requirements are likely to become even more severe due to the rapid growth in the size of these databases. In this paper, we present a new approach to bio-sequence database scanning using computer graphics hardware to gain high performance at low cost. To derive an efficient mapping onto this type of architecture, we have reformulated the Smith-Waterman dynamic programming algorithm in terms of computer graphics primitives. Our OpenGL implementation achieves a speedup of approximately sixteen on a high-end graphics card over available straightforward and optimized CPU Smith-Waterman implementations.

/pdf/bio-sequence-database-scanning-on-a-gpu-1pflp8be6y.pdf

Bio-sequence database scanning on a GPU

Scanning protein sequence database is an often repeated task in computational biology and bioinformatics. However, scanning large protein databases, such as GenBank, with popular tools such as BLASTP requires long runtimes on sequential architectures. Due to the continuing rapid growth of sequence databases, there is a high demand to accelerate this task. In this paper, we demonstrate how GPUs, powered by the Compute Unified Device Architecture (CUDA), can be used as an efficient computational platform to accelerate the BLASTP algorithm. In order to exploit the GPU's capabilities for accelerating BLASTP, we have used a compressed deterministic finite state automaton for hit detection as well as a hybrid parallelization scheme. Our implementation achieves speedups up to 10.0 on an NVIDIA GeForce GTX 295 GPU compared to the sequential NCBI BLASTP 2.2.22. CUDA-BLASTP source code which is available at https://sites.google.com/site/liuweiguohome/software.

CUDA-BLASTP: Accelerating BLASTP on CUDA-Enabled Graphics Hardware

Emerging DNA sequencing technologies open up exciting new opportunities for genome sequencing by generating read data with a massive throughput. However, produced reads are significantly shorter and more error-prone compared to the traditional Sanger shotgun sequencing method. This poses challenges for de novo DNA fragment assembly algorithms in terms of both accuracy (to deal with short, error-prone reads) and scalability (to deal with very large input data sets). In this article, we present a scalable parallel algorithm for correcting sequencing errors in high-throughput short-read data so that error-free reads can be available before DNA fragment assembly, which is of high importance to many graph-based short-read assembly tools. The algorithm is based on spectral alignment and uses the Compute Unified Device Architecture (CUDA) programming model. To gain efficiency we are taking advantage of the CUDA texture memory using a space-efficient Bloom filter data structure for spectrum membership queries. We have tested the runtime and accuracy of our algorithm using real and simulated Illumina data for different read lengths, error rates, input sizes, and algorithmic parameters. Using a CUDA-enabled mass-produced GPU (available for less than US$400 at any local computer outlet), this results in speedups of 12-84 times for the parallelized error correction, and speedups of 3-63 times for both sequential preprocessing and parallelized error correction compared to the publicly available Euler-SR program. Our implementation is freely available for download from http://cuda-ec.sourceforge.net .

Wolfgang Müller-Wittig

Papers

Streaming Algorithms for Biological Sequence Alignment on GPUs

Accelerating molecular dynamics simulations using Graphics Processing Units with CUDA

Bio-sequence database scanning on a GPU

CUDA-BLASTP: Accelerating BLASTP on CUDA-Enabled Graphics Hardware

A parallel algorithm for error correction in high-throughput short-read data on CUDA-enabled graphics hardware