Dataset Information

GPU-enabled FREALIGN: accelerating single particle 3D reconstruction and refinement in Fourier space on graphics processors.

ABSTRACT: Among all the factors that determine the resolution of a 3D reconstruction by single particle electron cryo-microscopy (cryoEM), the number of particle images used in the dataset plays a major role. More images generally yield better resolution, assuming the imaged protein complex is conformationally and compositionally homogeneous. To facilitate processing of very large datasets, we modified the computer program, FREALIGN, to execute the computationally most intensive procedures on Graphics Processing Units (GPUs). Using the modified program, the execution speed increased between 10 and 240-fold depending on the task performed by FREALIGN. Here we report the steps necessary to parallelize critical FREALIGN subroutines and evaluate its performance on computers with multiple GPUs.

SUBMITTER: Li X

PROVIDER: S-EPMC2964447 | biostudies-literature | 2010 Dec

REPOSITORIES: biostudies-literature

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GPU-enabled FREALIGN: accelerating single particle 3D reconstruction and refinement in Fourier space on graphics processors.

Li Xueming X Grigorieff Nikolaus N Cheng Yifan Y

Journal of structural biology 20100615 3

Among all the factors that determine the resolution of a 3D reconstruction by single particle electron cryo-microscopy (cryoEM), the number of particle images used in the dataset plays a major role. More images generally yield better resolution, assuming the imaged protein complex is conformationally and compositionally homogeneous. To facilitate processing of very large datasets, we modified the computer program, FREALIGN, to execute the computationally most intensive procedures on Graphics Pro ...[more]

PMID: 20558298

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Project description:HMMER software suite is widely used for analysis of homologous protein and nucleotide sequences with high sensitivity. The latest version of hmmsearch in HMMER 3.x, utilizes heuristic-pipeline which consists of MSV/SSV (Multiple/Single ungapped Segment Viterbi) stage, P7Viterbi stage and the Forward scoring stage to accelerate homology detection. Since the latest version is highly optimized for performance on modern multi-core CPUs with SSE capabilities, only a few acceleration attempts report speedup. However, the most compute intensive tasks within the pipeline (viz., MSV/SSV and P7Viterbi stages) still stand to benefit from the computational capabilities of massively parallel processors.A Multi-Tiered Parallel Framework (CUDAMPF) implemented on CUDA-enabled GPUs presented here, offers a finer-grained parallelism for MSV/SSV and Viterbi algorithms. We couple SIMT (Single Instruction Multiple Threads) mechanism with SIMD (Single Instructions Multiple Data) video instructions with warp-synchronism to achieve high-throughput processing and eliminate thread idling. We also propose a hardware-aware optimal allocation scheme of scarce resources like on-chip memory and caches in order to boost performance and scalability of CUDAMPF. In addition, runtime compilation via NVRTC available with CUDA 7.0 is incorporated into the presented framework that not only helps unroll innermost loop to yield upto 2 to 3-fold speedup than static compilation but also enables dynamic loading and switching of kernels depending on the query model size, in order to achieve optimal performance.CUDAMPF is designed as a hardware-aware parallel framework for accelerating computational hotspots within the hmmsearch pipeline as well as other sequence alignment applications. It achieves significant speedup by exploiting hierarchical parallelism on single GPU and takes full advantage of limited resources based on their own performance features. In addition to exceeding performance of other acceleration attempts, comprehensive evaluations against high-end CPUs (Intel i5, i7 and Xeon) shows that CUDAMPF yields upto 440 GCUPS for SSV, 277 GCUPS for MSV and 14.3 GCUPS for P7Viterbi all with 100 % accuracy, which translates to a maximum speedup of 37.5, 23.1 and 11.6-fold for MSV, SSV and P7Viterbi respectively. The source code is available at https://github.com/Super-Hippo/CUDAMPF.

Dataset Information

GPU-enabled FREALIGN: accelerating single particle 3D reconstruction and refinement in Fourier space on graphics processors.

Publications

GPU-enabled FREALIGN: accelerating single particle 3D reconstruction and refinement in Fourier space on graphics processors.

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