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1 tems in approximately one second on a single graphics processing unit.
2 ut losing calculation precision on an NVIDIA graphics processing unit.
3 method, we parallelized the computation on a graphics processing unit.
4 -time data processing being accelerated by a graphics-processing unit.
5  fits regularized regression across multiple Graphics Processing Units.
6             We developed deoxyribozyme-based graphics processing units able to monitor nucleic acids
7 mpute Unified Device Architecture-compatible graphics processing units and deep learning techniques s
8                                          Our graphics processing unit based software delivers haploty
9 to parallel computing architectures, such as graphics processing units by illustrating its utility fo
10         Our novel, efficient algorithm using graphics processing units can accurately characterize bo
11  we demonstrate that parallel computation on graphics processing units can reduce the processing time
12 dial fluctuations (SRRF), provided as a fast graphics processing unit-enabled ImageJ plugin.
13 es at their disposal, and recent advances in graphics processing unit (GPU) computing have added a pr
14 aster than qcprot and 3x faster than current graphics processing unit (GPU) implementations.
15 ice for fast MicroRNA-Seq data analysis in a graphics processing unit (GPU) infrastructure.
16 urden between multiple processor cores and a graphics processing unit (GPU) simultaneously.
17                      Using a general-purpose graphics processing unit (GPU), we have developed GPU-BL
18  multicore architecture of a modern consumer graphics processing unit (GPU), we report a 92x increase
19 arallelism present in modern traditional and graphics processing unit (GPU)-accelerated machines, fro
20                                         With graphics processing unit (GPU)-based CUDA C/C++ implemen
21 cence lifetimes, Stokes shifts, and extended graphics processing unit (GPU)-based quantum mechanics/m
22 lculation of ESP and (ii) its mapping onto a graphics processing unit (GPU).
23 it is now possible to exploit the power of a graphics-processing unit (GPU) from a browser without an
24 hics hardware, a crucial feature that allows graphics-processing-unit (GPU)-based processing for inte
25                                              Graphics processing units (GPUs) are capable of efficien
26 e algorithm is implemented on ATI and nVidia graphics processing units (GPUs) for maximal speed.
27  new software tool STOCHSIMGPU that exploits graphics processing units (GPUs) for parallel stochastic
28                                              Graphics processing units (GPUs) provide an inexpensive
29                                     By using graphics processing units (GPUs) the time needed to buil
30 r arbitrary molecular evolutionary models on graphics processing units (GPUs), making use of the larg
31                                              Graphics processing units (GPUs), the hardware responsib
32                                          The Graphics Processing Unit implementation of the algorithm
33 ning read-outs of molecular states that uses graphics processing units made from molecular circuits.
34 oth in the central processing unit (CPU) and Graphics processing unit processor markets, enabling mas
35                              These molecular graphics processing units provide insight for the constr
36   Implementation of these algorithms for the graphics processing unit results in dramatic speedup of
37 ness the computational power of NVIDIA GPUs (Graphics Processing Units) to greatly reduce processing
38 tic model: the Bayesian network algorithm, a graphics processing unit version of the Bayesian network
39 rallel computational power of a programmable graphics processing unit with the flexibility of the dyn

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