Abdul Dakkak
Abdul Dakkak
Microsoft Research
Verified email at microsoft.com - Homepage
Title
Cited by
Cited by
Year
Recovering missing depth information from Microsoft’s Kinect
A Dakkak, A Husain
Proc. Embedded Vis. Alliance, 1-9, 2012
182012
Accelerating reduction and scan using tensor core units
A Dakkak, C Li, J Xiong, I Gelado, W Hwu
Proceedings of the ACM International Conference on Supercomputing, 46-57, 2019
172019
Triolet: A programming system that unifies algorithmic skeleton interfaces for high-performance cluster computing
C Rodrigues, T Jablin, A Dakkak, WM Hwu
ACM SIGPLAN Notices 49 (8), 247-258, 2014
142014
Enhancing the usability and utilization of accelerated architectures via docker
N Haydel, S Gesing, I Taylor, G Madey, A Dakkak, SG De Gonzalo, ...
2015 IEEE/ACM 8th International Conference on Utility and Cloud Computing …, 2015
122015
Tangram: a high-level language for performance portable code synthesis
LW Chang, A Dakkak, CI Rodrigues, W Hwu
Programmability Issues for Heterogeneous Multicores, 2015
122015
Trims: Transparent and isolated model sharing for low latency deep learning inference in function-as-a-service
A Dakkak, C Li, SG De Gonzalo, J Xiong, W Hwu
2019 IEEE 12th International Conference on Cloud Computing (CLOUD), 372-382, 2019
92019
Webgpu: A scalable online development platform for gpu programming courses
A Dakkak, C Pearson, W Hwu
2016 IEEE International Parallel and Distributed Processing Symposium …, 2016
92016
Evaluating characteristics of CUDA communication primitives on high-bandwidth interconnects
C Pearson, A Dakkak, S Hashash, C Li, IH Chung, J Xiong, WM Hwu
Proceedings of the 2019 ACM/SPEC International Conference on Performance …, 2019
72019
Frustrated with replicating claims of a shared model? a solution
A Dakkak, C Li, J Xiong, WM Hwu
arXiv preprint arXiv:1811.09737, 2018
52018
Mlmodelscope: Evaluate and measure ml models within ai pipelines
A Dakkak, C Li, A Srivastava, J Xiong, WM Hwu
arXiv preprint arXiv:1811.09737, 2018
52018
A programming system for future proofing performance critical libraries
LW Chang, I El Hajj, HS Kim, J Gómez-Luna, A Dakkak, W Hwu
ACM SIGPLAN Notices 51 (8), 1-2, 2016
52016
Transitioning HPC software to exascale heterogeneous computing
WM Hwu, LW Chang, HS Kim, A Dakkak, I El Hajj
2015 Computational Electromagnetics International Workshop (CEM), 1-2, 2015
52015
Across-stack profiling and characterization of machine learning models on GPUs
C Li, A Dakkak, J Xiong, W Wei, L Xu, WM Hwu
Unknown Journal, 2019
42019
RAI: A scalable project submission system for parallel programming courses
A Dakkak, C Pearson, C Li, W Hwu
2017 IEEE International Parallel and Distributed Processing Symposium …, 2017
42017
Xsp: Across-stack profiling and analysis of machine learning models on gpus
C Li, A Dakkak, J Xiong, W Wei, L Xu, W Hwu
2020 IEEE International Parallel and Distributed Processing Symposium (IPDPS …, 2020
32020
Thoughts on massively-parallel heterogeneous computing for solving large problems
W Hwu, M Hidayetoglu, WC Chew, C Pearson, S Garcia, S Huang, ...
2017 Computing and Electromagnetics International Workshop (CEM), 67-68, 2017
32017
Benanza: Automatic μBenchmark Generation to Compute" Lower-bound" Latency and Inform Optimizations of Deep Learning Models on GPUs
C Li, A Dakkak, J Xiong, W Hwu
2020 IEEE International Parallel and Distributed Processing Symposium (IPDPS …, 2020
22020
The design and implementation of a scalable dl benchmarking platform
C Li, A Dakkak, J Xiong, W Hwu
arXiv preprint arXiv:1911.08031, 2019
22019
Challenges and pitfalls of reproducing machine learning artifacts
C Li, A Dakkak, J Xiong, W mei Hwu
CoRR, abs/1904.12437, 2019
22019
SCOPE: C3SR Systems Characterization and Benchmarking Framework
C Pearson, A Dakkak, C Li, S Hashash, J Xiong, W Hwu
arXiv preprint arXiv:1809.08311, 2018
12018
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