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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 odestly powered computers lacking a discrete graphics processing unit.
5 ,000 structures per day on a server with one graphics processing unit.
6 20 x 1,080 pixels on a single consumer-grade graphics processing unit.
7 -time data processing being accelerated by a graphics-processing unit.
8  fits regularized regression across multiple Graphics Processing Units.
9 tate-of-the-art digital counterparts such as graphics processing units.
10 ined and a coarse-grained parallelization on Graphics Processing Units.
11             We developed deoxyribozyme-based graphics processing units able to monitor nucleic acids
12                             MEDUSA uses GPU (graphics processing unit) accelerated hardware and a par
13 Our theoretical model is implemented in GPU (Graphics Processing Unit) accelerated software which can
14 iology, we previously developed emClarity, a graphics processing unit-accelerated image-processing so
15                       Efficiently harnessing graphics processing unit acceleration along with systema
16 to-use Python software package and leverages graphics processing unit acceleration when available.
17 ffects, stochastic variational inference and graphics processing unit acceleration.
18 l cardiac cine series took 610 msec with the graphics processing unit and 5.6 seconds with central pr
19                   The mass production of the graphics processing unit and the coronavirus disease 201
20 mpute Unified Device Architecture-compatible graphics processing units and deep learning techniques s
21  < .001); inference run time of 0.99 second (graphics processing unit) and 2.27 seconds (central proc
22 nitude greater than that of state-of-the-art graphics-processing units, and is shown to be scalable t
23   Practical benefits varied across different graphics processing unit architectures, with more distin
24                                          Our graphics processing unit based software delivers haploty
25 to parallel computing architectures, such as graphics processing units by illustrating its utility fo
26         Our novel, efficient algorithm using graphics processing units can accurately characterize bo
27  we demonstrate that parallel computation on graphics processing units can reduce the processing time
28 erates optimized central processing unit and graphics processing unit code variations, learning and p
29 PU threads and multiple GPU (general-purpose graphics processing unit) devices.
30 dial fluctuations (SRRF), provided as a fast graphics processing unit-enabled ImageJ plugin.
31 -dimensional image-based registration with a graphics processing unit enhances processing speed 10- t
32 es at their disposal, and recent advances in graphics processing unit (GPU) computing have added a pr
33                                              Graphics processing unit (GPU) computing is an affordabl
34                                            A Graphics Processing Unit (GPU) implementation and downsa
35 aster than qcprot and 3x faster than current graphics processing unit (GPU) implementations.
36 ice for fast MicroRNA-Seq data analysis in a graphics processing unit (GPU) infrastructure.
37 urden between multiple processor cores and a graphics processing unit (GPU) simultaneously.
38  image classification network, relative to a graphics processing unit (GPU) that uses a comparable 12
39 ents those methods in a parallel manner on a graphics processing unit (GPU) using CUDA platform.
40                      Using a general-purpose graphics processing unit (GPU), we have developed GPU-BL
41  multicore architecture of a modern consumer graphics processing unit (GPU), we report a 92x increase
42 arallelism present in modern traditional and graphics processing unit (GPU)-accelerated machines, fro
43                                     Here the graphics processing unit (GPU)-accelerated MMseqs2 deliv
44                                         With graphics processing unit (GPU)-based CUDA C/C++ implemen
45 cence lifetimes, Stokes shifts, and extended graphics processing unit (GPU)-based quantum mechanics/m
46 thm for parallel execution on an NVIDIA V100 graphics processing unit (GPU).
47 lculation of ESP and (ii) its mapping onto a graphics processing unit (GPU).
48 emerging and readily available in the recent graphics processing unit (GPU).
49  independent structures per hour on a single graphics processing unit (GPU).
50 st possible greedy algorithms accelerated on Graphics Processing Units (GPU), scaled magnetic version
51                      We present I-TASSER for Graphics Processing Units (GPU-I-TASSER), a GPU accelera
52 it is now possible to exploit the power of a graphics-processing unit (GPU) from a browser without an
53 hics hardware, a crucial feature that allows graphics-processing-unit (GPU)-based processing for inte
54 ultiple target hardware platforms, including Graphics Processing Units (GPUs) and Field Programmable
55 rt all BLS parameters that take advantage of graphics processing units (GPUs) and result in many fold
56                                              Graphics processing units (GPUs) are capable of efficien
57 e algorithm is implemented on ATI and nVidia graphics processing units (GPUs) for maximal speed.
58  new software tool STOCHSIMGPU that exploits graphics processing units (GPUs) for parallel stochastic
59 f traditional CPU computing infrastructures, Graphics Processing Units (GPUs) offer opportunities to
60                                              Graphics processing units (GPUs) provide an inexpensive
61                                     Although graphics processing units (GPUs) provide high performanc
62  paper is adding a full support for multiple graphics processing units (GPUs) support, which makes it
63                                     By using graphics processing units (GPUs) the time needed to buil
64 on with [Formula: see text], we use multiple graphics processing units (GPUs) to compute the real fre
65 ed code via multiple threads in parallel, on graphics processing units (GPUs) using NVidia Compute Un
66                   With the popularization of graphics processing units (GPUs), GPU-compatible sequenc
67 lel computing hardware including inexpensive graphics processing units (GPUs), it has remained infeas
68 r arbitrary molecular evolutionary models on graphics processing units (GPUs), making use of the larg
69 eneral-purpose libraries for computing using graphics processing units (GPUs), such as PyTorch and Te
70                                              Graphics processing units (GPUs), the hardware responsib
71            Thanks to a very intensive use of graphics processing units (GPUs), we have been able to r
72 ons using consumer or high performance grade graphics processing units (GPUs).
73 t multiple CPU cores but also one or several graphics processing units (GPUs).
74 d with CPU + GPU (Central Processing Units + Graphics Processing Units) heterogeneous parallel comput
75  the rapid development of massively parallel Graphics Processing Unit, i.e. the GPU computing technol
76                                          The Graphics Processing Unit implementation of the algorithm
77 ning read-outs of molecular states that uses graphics processing units made from molecular circuits.
78 entional general-purpose processors, such as graphics processing units or central processing units.
79 oth in the central processing unit (CPU) and Graphics processing unit processor markets, enabling mas
80                              These molecular graphics processing units provide insight for the constr
81   Implementation of these algorithms for the graphics processing unit results in dramatic speedup of
82 ness the computational power of NVIDIA GPUs (Graphics Processing Units) to greatly reduce processing
83 tic model: the Bayesian network algorithm, a graphics processing unit version of the Bayesian network
84 rallel computational power of a programmable graphics processing unit with the flexibility of the dyn