( en | de | ru )

 

Disambiguation

From post

копироватьудалитьдобавить публикацию в буфер
A benchmark set of highly-efficient CUDA and OpenCL kernels and its dynamic autotuning with Kernel Tuning Toolkit.

, , , , , , и . Future Gener. Comput. Syst., (2020)

Please choose a person to relate this publication to

To differ between persons with the same name, the academic degree and the title of an important publication will be displayed.

Visvaldis Janavs

Reinhard Janas

Vinod Janardhanan

Mohammad al- Janabi

Maria Janacek-Alex

 

Другие публикации лиц с тем же именем

Using hardware performance counters to speed up autotuning convergence on GPUs., , , , и . CoRR, (2021)Improving ligand transport trajectory within flexible receptor in CaverDock., , и . SAC, стр. 619-626. ACM, (2022)Exploiting historical data: Pruning autotuning spaces and estimating the number of tuning steps., , , и . Concurr. Comput. Pract. Exp., (2020)Using hardware performance counters to speed up autotuning convergence on GPUs., , , , и . J. Parallel Distributed Comput., (2022)FAIR Sharing of Data in Autotuning Research (Vision Paper)., , , , и . ICPE (Companion), стр. 21-27. ACM, (2024)A Benchmark Set of Highly-efficient CUDA and OpenCL Kernels and its Dynamic Autotuning with Kernel Tuning Toolkit., , , , , , и . CoRR, (2019)Searching CUDA code autotuning spaces with hardware performance counters: data from benchmarks running on various GPU architectures., , , , и . CoRR, (2021)Property Map Collective Variable as a Useful Tool for a Force Field Correction., , , , , , и . J. Chem. Inf. Model., 62 (3): 567-576 (2022)A benchmark set of highly-efficient CUDA and OpenCL kernels and its dynamic autotuning with Kernel Tuning Toolkit., , , , , , и . Future Gener. Comput. Syst., (2020)Exploiting Historical Data: Pruning Autotuning Spaces and Estimating the Number of Tuning Steps., , , и . Euro-Par Workshops, том 11997 из Lecture Notes in Computer Science, стр. 295-307. Springer, (2019)