Gerrit Voss

Streaming Algorithms for Biological Sequence Alignment on GPUs

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.

Accelerating molecular dynamics simulations using Graphics Processing Units with CUDA

Molecular dynamics is an important computational tool to simulate and understand biochemical processes at the atomic level. However, accurate simulation of processes such as protein folding requires a large number of both atoms and time steps. This in turn leads to huge runtime requirements. Hence, finding fast solutions is of highest importance to research. In this paper we present a new approach to accelerate molecular dynamics simulations with inexpensive commodity graphics hardware. To derive an efficient mapping onto this type of computer architecture, we have used the new Compute Unified Device Architecture programming interface to implement a new parallel algorithm. Our experimental results show that the graphics card based approach allows speedups of up to factor nineteen compared to the corresponding sequential implementation.

Bio-sequence database scanning on a GPU

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

Molecular dynamics simulations on commodity GPUs with CUDA

Molecular dynamics simulations are a common and often repeated task in molecular biology. The need for speeding up this treatment comes from the requirement for large system simulations with many atoms and numerous time steps. In this paper we present a new approach to high performance molecular dynamics simulations on graphics processing units. Using modern graphics processing units 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 used the Compute Unified Device Architecture (CUDA) to design and implement a new parallel algorithm. This results in an implementation with significant runtime savings on an off-the-shelf computer graphics card.

GPU-ClustalW: using graphics hardware to accelerate multiple sequence alignment

Molecular Biologists frequently compute multiple sequence alignments (MSAs) to identify similar regions in protein families. However, aligning hundreds of sequences by popular MSA tools such as ClustalW requires several hours on sequential computers. Due to the rapid growth of biological sequence databases biologists have to compute MSAs in a far shorter time. In this paper we present a new approach to reduce this runtime using graphics processing units (GPUs). To derive an efficient mapping onto this type of architecture, we have reformulated the computationally most expensive part of ClustalW in terms of computer graphics primitives. This results in a high-speed implementation with significant runtime savings on a commodity graphics card.

Augmented reality for enhancement of endoscopic interventions

Computer assisted operation planning systems win more and more recognition in the field of surgery. These systems offer new possibilities to prepare an intervention with the goal to shorten the expansive time in the operation room required for the intervention. The safest and most effective surgical approach should be selected. But often, it is difficult to transfer the output of the planning system to the intra-operative situation and so to consider the planning results in the real intervention. At the Fraunhofer Institute for Computer Graphics (IGD) in Darmstadt and the Centre for Advanced Media Technology (CAMTech) in Singapore, methods are developed to bridge the gap between the external planning session and the intra-operative case: augmented reality (AR) techniques are used to overlap preoperative scanned image data as well as results of the planning session to the operation field.

Mapping of BLASTP Algorithm onto GPU Clusters

Searching protein sequence database is a fundamental and often repeated task in computational biology and bioinformatics. However, the high computational cost and long runtime of many database scanning algorithms on sequential architectures heavily restrict their applications for large-scale protein databases, such as GenBank. The continuing exponential growth of sequence databases and the high rate of newly generated queries further deteriorate the situation and establish a strong requirement for time-efficient scalable database searching algorithms. In this paper, we demonstrate how GPU clusters, powered by the Compute Unified Device Architecture (CUDA), OpenMP, and MPI parallel programming models can be used as an efficient computational platform to accelerate the popular BLASTP algorithm. Compared to GPU-BLAST 1.0-2.2.24, our implementation achieves speedups up to 1.6 on a single GPU and up to 6.6 on the 6 GPUs of a Tesla S1060 quad-GPU computing system. The source code is available at: http://sites.google.com/site/liuweiguohome/mpicuda-blastp

GCMR: A GPU Cluster-Based MapReduce Framework for Large-Scale Data Processing

MapReduce is a very popular programming model to support parallel and distributed large-scale data processing. There have been a lot of efforts to implement this model on commodity GPU-based systems. However, most of these implementations can only work on a single GPU. And they can not be used to process large-scale datasets. In this paper, we present a new approach to design the MapReduce framework on GPU clusters for handling large-scale data processing. We have used Compute Unified Device Architectures (CUDA) and MPI parallel programming models to implement this framework. To derive an efficient mapping onto GPU clusters, we introduce a two-level parallelization approach: the inter node level and intra node level parallelization. Furthermore in order to improve the overall MapReduce efficiency, a multi-threading scheme is used to overlap the communication and computation on a multi-GPU node. Compared to previous GPU-based MapReduce implementations, our implementation, called GCMR, achieves speedups up to 2.6 on a single node and up to 9.1 on 4 nodes of a Tesla S1060 quad-GPU cluster system for processing small datasets. It also shows very good scalability for processing large-scale datasets on the cluster system.

Cultural heritage as digital experience: a Singaporean perspective

Interactive 3D computer graphics technology is now extremely popular, seen in the increasing interest in and use of 3D digitization of cultural heritage contents. This paper introduces a digital interactive cultural heritage system which embeds Virtual-Reality (VR) technology within the Peranakan culture, the Peranakan culture being a Singaporean unique ethnic culture. A prototype has undergone the full developmental process of being implemented, tested, and evaluated. This paper will also discuss the results of the usability test.

Climbing Longs Peak: The Steep Road to the Future of OpenGL

An essential goal of OpenGL is to provide device independence while still allowing complete access to hardware functionality. The API therefore provides access to graphics operations at the lowest possible level that still provides device independence. In OpenGL, the maximum flexibility provided by individual procedure calls was deemed more important than any inefficiency induced by using those calls. Backward compatibility was seen as one of OpenGLs core strengths, compared to other low-level 3D graphics APIs. All of these extensions were driven by developments in the graphics hardware arena, which changed significantly over the years. The road up Longs Peak can be fairly long and rocky, depending on where you must start. But the view is terrific, and it goes a long way into the future. So, its definitely worth it. OpenGL Longs Peak will come, and the need for supporting infrastructure will be there.