Results 231 to 240 of about 28,341 (292)

Gpu-accelerated JEMRIS for extensive MRI simulations. [PDF]

MAGMA
Nurdinova A, Ruschke S, Gestrich M, Stelter J, Karampinos DC.   +4 more
europepmc   +1 more source

PowerNovo2: A generative flow-based approach to non-autoregressive de novo peptide sequencing. [PDF]

PLoS Comput Biol
Petrovskiy DV, Nikolsky KS, Rudnev VR, Kulikova LI, Butkova TV, Malsagova KA, Kopylov AT, Kaysheva AL.   +7 more
europepmc   +1 more source

Smart medical system integrating clinical workflows for robust skin cancer detection across heterogeneous pathologies. [PDF]

Sci Rep
Abugabah A, Shukla PK, Mishra S, Dwivedi A.   +3 more
europepmc   +1 more source

Neurofilament Light Chain from Neuronally Derived Extracellular Vesicles in Differentiating Parkinson's Disease from Essential Tremor with Resting Tremor. [PDF]

Mol Neurobiol
Mimmi S, Cristiani CM, Talarico M, Tolomeo AM, Parrotta EI, Scaramuzzino L, Crapella V, Pingitore E, Iaccino E, Cuda G, Quattrone A, Quattrone A.   +11 more
europepmc   +1 more source

Cuda Polski

, 2014
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Rzadkie cuda

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CUDA Flux: A Lightweight Instruction Profiler for CUDA Applications

2019 IEEE/ACM Performance Modeling, Benchmarking and Simulation of High Performance Computer Systems (PMBS), 2019
GPUs are powerful, massively parallel processors, which require a vast amount of thread parallelism to keep their thousands of execution units busy, and to tolerate latency when accessing its high-throughput memory system. Understanding the behavior of massively threaded GPU programs can be difficult, even though recent GPUs provide an abundance of ...
Lorenz Braun, Holger Fröning
openaire   +1 more source

Debugging CUDA

Proceedings of the 13th annual conference companion on Genetic and evolutionary computation, 2011
During six months of intensive nVidia CUDA C programming many bugs were created. We pass on the software engineering lessons learnt, particularly those relevant to parallel general-purpose computation on graphics hardware GPGPU.
openaire   +1 more source

Porting Autodock to CUDA

IEEE Congress on Evolutionary Computation, 2010
This paper is a report on the migration of the molecular docking application, “Autodock” to NVIDIA CUDA. Autodock is a Drug Discovery Tool that uses a Genetic Algorithm to find the optimal docking position of a ligand to a protein. Speedup of Autodock greatly benefits the drug discovery process.
Sarnath Kannan, Raghavendra Ganji
openaire   +1 more source