ライフサイエンスコーパス: GPU

1 e gas conditions with CO2 permeance of 1,020 GPU and CO2/N2 selectivity as high as 680, demonstrating

2 3.5x10(-7) mol m(-2) s(-1) Pa(-1) (ca. 1000 GPU).

3 We demonstrate the new software using 1024 GPUs in parallel on one of the world's fastest supercomp

4 from 4 to 10 (resp. With 2 CPU threads and 2 GPUs, H-BLAST can be faster than 16-threaded NCBI-BLASTX

5 -score-GPU is 68 times faster on an ATI 5870 GPU, on average, than the original CPU single-threaded i

6 a user-friendly interface between cupSODA, a GPU-powered kinetic simulator, and PySB, a Python-based

7 n achieve performance that exceeds that of a GPU-based coprocessor.

8 ate how the cost of divergent branching on a GPU can be amortized across algorithms like HCP in order

9 Here we propose a GPU-accelerated biclustering algorithm, based on searchi

10 'Anton' machine and large ensembles of AMBER GPU simulations.

11 an automated workflow tool to perform AMBER GPU MD simulations.

12 and user-friendly environment and the AMBER GPU code for a robust and high-performance simulation en

13 In our experiments with a four core CPU and GPU, SWIFTLINK achieves a 8.5x speed-up over the single-

14 s free, open-source, platformindependent and GPU-vendor independent.

15 ke advantage of both multiple processors and GPU-acceleration to perform the numerically-demanding co

16 for better efficiency among various CPUs and GPUs combinations.

17 rted hardware types (including both CPUs and GPUs) and perform well on all of them.

18 heterogeneous computer that couples CPUs and GPUs, to accelerate BLASTX and BLASTP-basic tools of NCB

19 coprocessor architectures such as FPGAs and GPUs.

20 esigned to run on any commercially available GPU and on any operating system.

21 We present a C-based, GPU-accelerated implementation of nested sampling that i

22 nstrate dramatic speed differentials between GPU assisted performance and CPU executions as the compu

23 differences in hardware architecture between GPUs and CPUs complicate the porting of existing code.

24 uential NCBI-BLAST, the speedups achieved by GPU-BLAST range mostly between 3 and 4.

25 rty seconds when using an NVIDIA Tesla C2050 GPU.

26 Combining GPU and HCP, resulted in a speedup of at most 1,860-fold

27 mplementation utilizing multicore hybrid CPU/GPU computing resources, which can process terabytes of

28 of biochemical network models on NVIDIA CUDA GPUs.

29 ics processing unit (GPU), we have developed GPU-BLAST, an accelerated version of the popular NCBI-BL

30 mework (CUDAMPF) implemented on CUDA-enabled GPUs presented here, offers a finer-grained parallelism

31 sion of the code able to efficiently exploit GPU-accelerated systems for both the genetic relationshi

32 Extended GPU-based computational studies of a ternary complex con

33 d in microarray gene expression analysis for GPU-equipped computers.

34 parallelized CCS algorithm using CUDA C for GPU computing.

35 The implementation of MDR for GPUs (MDRGPU) includes core features of the widely used

36 tion approaches going all the way up to full GPU-accelerated ray tracing, they do not provide size-sp

37 In this paper, we present the GeNN (GPU-enhanced Neuronal Networks) framework, which aims to

38 Massively Parallel Architectures, Including GPU Nodes (CAMPAIGN), a central resource for data cluste

39 out using the Amber software on inexpensive GPUs, providing approximately 1 mus/day per GPU, and >2.

40 fically, we characterize the effect of known GPU optimization techniques like use of shared memory.

41 Mendel-GPU, our OpenCL software, runs on Linux platforms and is

42 New GPU code further increases the speed of RMSD and TM-scor

43 the better performance provided by an Nvidia GPU.

44 ithub.com/aresio/cupSODA (requires an Nvidia GPU; developer.nvidia.com/cuda-gpus).

45 PDB file for pocket detection to our NVIDIA GPU-equipped servers through a WebGL graphical interface

46 s, and OpenCL kernels support AMD and Nvidia GPUs.

47 tforms and is portable across AMD and nVidia GPUs.

48 nd tools, written in 'C for CUDA' for Nvidia GPUs.

49 to harness the computational power of NVIDIA GPUs (Graphics Processing Units) to greatly reduce proce

50 e execution of network simulations on NVIDIA GPUs, through a flexible and extensible interface, which

51 ementations negate the apparent advantage of GPU implementations.

52 comparisons inflate the actual advantages of GPU technology.

53 The source code of GPU-BLAST is freely available at http://archimedes.cheme

54 No knowledge of GPU computing is required from the user.

55 The use of GPU does not deteriorate the accuracy of our results.

56 Embracing the multicore functionality of GPUs represents a major avenue of local accelerated comp

57 dynamics simulations in implicit solvent on GPU processors were used to generate ensembles of trajec

58 xtension algorithm for better performance on GPUs, and offers a performance tuning mechanism for bett

59 Memory requirements on GPUs have been reduced to fit widely available hardware,

60 GPUs, providing approximately 1 mus/day per GPU, and >2.5 ms data presented here.

61 tients undergoing esophagectomy with planned GPU reconstruction.

62 simulations, run using graphical processors (GPUs), were used to investigate the effect of conformati

63 sing software that uses graphics processors (GPUs) to address the most computationally intensive step

64 ual Xeon 5520 and 3X speedup versus BEAGLE's GPU implementation on a Tesla T10 GPU for very large dat

65 TM-score-GPU is 68 times faster on an ATI 5870 GPU, on average, t

66 TM-score-GPU was applied to six sets of models from Nutritious Ri

67 ore for Graphical Processing Units (TM-score-GPU), using a new and novel hybrid Kabsch/quaternion met

68 search process can be performed on a single GPU in a massively parallel fashion.

69 xploiting hierarchical parallelism on single GPU and takes full advantage of limited resources based

70 s BEAGLE's GPU implementation on a Tesla T10 GPU for very large data sizes.

71 thermore, H-BLAST is 1.5-4 times faster than GPU-BLAST.

72 The GPU implementation substantially decreases computational

73 The GPU-based parallel implementation of the Gillespie stoch

74 ve, single-threaded CPU implementations, the GPU yields a large improvement in the execution time.

75 The complete source, including the GPU code and the hybrid RMSD subroutine, can be download

76 by 25-fold solely by parallelization on the GPU.

77 g of biomolecules into a grid/lattice on the GPU.

78 urn, better facilitates its mapping onto the GPU.

79 obs-MPI using eight nodes is faster than the GPU-accelerated QuickProbs running on a Tesla K20.

80 Using the GPU to store and render the image sequence data enables

81 es interactively on the system CPU while the GPU handles 3-D visualization tasks.

82 This GPU implementation allows for large-scale analysis of ep

83 petitive processes and facilitates access to GPU clusters, whose high-performance processing power ma

84 recent advances in graphics processing unit (GPU) computing have added a promising new option.

85 oit the power of a graphics-processing unit (GPU) from a browser without any third-party plugin.

86 aster than current graphics processing unit (GPU) implementations.

87 data analysis in a graphics processing unit (GPU) infrastructure.

88 ver and distributed Graphic Processing Unit (GPU) parallel computations.

89 cessor cores and a graphics processing unit (GPU) simultaneously.

90 a general-purpose graphics processing unit (GPU), we have developed GPU-BLAST, an accelerated versio

91 a modern consumer graphics processing unit (GPU), we report a 92x increase in the speed of an exhaus

92 rn traditional and graphics processing unit (GPU)-accelerated machines, from workstations to supercom

93 With graphics processing unit (GPU)-based CUDA C/C++ implementation, this new attenuati

94 ifts, and extended graphics processing unit (GPU)-based quantum mechanics/molecular mechanics (QM/MM)

95 implemented in a graphical processing unit (GPU).

96 its mapping onto a graphics processing unit (GPU).

97 eature that allows graphics-processing-unit (GPU)-based processing for interactive data analysis, suc

98 Graphics processing units (GPUs) are capable of efficiently running highly parallel

99 on ATI and nVidia graphics processing units (GPUs) for maximal speed.

100 GPU that exploits graphics processing units (GPUs) for parallel stochastic simulations of biological/

101 Graphics processing units (GPUs) provide an inexpensive and computationally powerfu

102 By using graphics processing units (GPUs) the time needed to build these models is decreased

103 tionary models on graphics processing units (GPUs), making use of the large number of processing core

104 Graphics processing units (GPUs), the hardware responsible for rendering computer g

105 ty after esophagectomy with gastric pull-up (GPU).

106 ctive data mining of spectral features using GPU-based manipulation of the spectral distribution.

107 Using GPUs to run MDR on a genome-wide dataset allows for stat

108 ython, Matlab and Java as well as CPU versus GPU implementations.

109 get genes leveraging parallel computing with GPU devices, (iii) all-in-one analytics with novel featu