Results 121 to 130 of about 913 (176)

EpiDiP/NanoDiP: a versatile unsupervised machine learning edge computing platform for epigenomic tumour diagnostics. [PDF]

Acta Neuropathol Commun
Hench J, Hultschig C, Brugger J, Mariani L, Guzman R, Soleman J, Leu S, Benton M, Stec IM, Hench IB, Hoffmann P, Harter P, Weber KJ, Albers A, Thomas C, Hasselblatt M, Schüller U, Restelli L, Capper D, Hewer E, Diebold J, Kolenc D, Schneider UC, Rushing E, Della Monica R, Chiariotti L, Sill M, Schrimpf D, von Deimling A, Sahm F, Kölsche C, Tolnay M, Frank S.   +32 more
europepmc   +1 more source

Electrochemical Modeling Applied to Intercalation Phenomena Using Lattice Kinetic Monte Carlo Simulations: Galvanostatic Simulations. [PDF]

Entropy (Basel)
Gavilán-Arriazu EM, Ruderman A, Bederian C, Moran Vieyra E, Leiva EPM.   +4 more
europepmc   +1 more source

Fast and Accurate Sperm Detection Algorithm for Micro-TESE in NOA Patients. [PDF]

Bioengineering (Basel)
Mohamed M, Kachi K, Motoya K, Ikeuchi M.
europepmc   +1 more source

Toward widespread use of virtual trials in medical imaging innovation and regulatory science. [PDF]

Med Phys
Abadi E, Barufaldi B, Lago M, Badal A, Mello-Thoms C, Bottenus N, Wangerin KA, Goldburgh M, Tarbox L, Beaucage-Gauvreau E, Frangi AF, Maidment A, Kinahan PE, Bosmans H, Samei E.   +14 more
europepmc   +1 more source

ClarityTrack for multi object tracking via hierarchical association and environment specific cost matching. [PDF]

Sci Rep
Lee SE, Yang HS, Jung SH, Sim CB.
europepmc   +1 more source

Trapezoidal back projection for positron emission tomography reconstruction. [PDF]

EJNMMI Phys
Varnyú D, Paczári K, Szirmay-Kalos L.
europepmc   +1 more source

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Towards predicting GPGPU performance for concurrent workloads in Multi-GPGPU environment

Cluster Computing, 2020
General-purpose graphics processing units (GPGPUs) have been widely adapted to the industry due to the high parallelism of graphics processing units (GPUs) compared with central processing units (CPUs). Especially, a GPGPU device has been adopted for various scientific workloads which have high parallelism.
Sunggon Kim, Yongseok Son, Hyeonsang Eom   +2 more
exaly   +2 more sources

Trivial Bypassing in GPGPUs

IEEE Embedded Systems Letters, 2021
This letter presents trivial bypassing to detect and skip execution of trivial instructions in general-purpose graphics processing units (GPGPUs). During the execution of a program, a significant number of instructions are trivial; that is, the instructions do not need functional units for execution.
openaire   +1 more source

GPGPU Composition with OCaml

Proceedings of ACM SIGPLAN International Workshop on Libraries, Languages, and Compilers for Array Programming, 2014
GPGPU programming promises high performance. However, to achieve it, developers must overcome several challenges. The main ones are: write and use hyper-parallel kernels on GPU, manage memory transfers between CPU and GPU, and compose kernels, keeping individual performance of components while optimizing the global performance.
Bourgoin, Mathias, Chailloux, Emmanuel
openaire   +2 more sources