Bounding the effect of partition camping in GPU kernels. - Ashwin M. Aji; Mayank Daga; Wu-chun Feng (Conf. Computing Frontiers, 2011)

Towards accelerating molecular modeling via multi-scale approximation on a GPU. - Mayank Daga; Wu-chun Feng; Thomas Scogland (ICCABS, 2011)

Architecture-Aware Mapping and Optimization on a 1600-Core GPU. - Mayank Daga; Thomas Scogland; Wu-chun Feng (ICPADS, 2011)

Exploiting Coarse-Grained Parallelism in B+ Tree Searches on an APU. - Mayank Daga; Mark Nutter (SC Companion, 2012)

Multi-dimensional characterization of electrostatic surface potential computation on graphics processors. - Mayank Daga; Wu-chun Feng (BMC Bioinform., 2012)

Efficient breadth-first search on a heterogeneous processor. - Mayank Daga; Mark Nutter; Mitesh R. Meswani (IEEE BigData, 2014)

Efficient Sparse Matrix-Vector Multiplication on GPUs Using the CSR Storage Format. - Joseph L. Greathouse; Mayank Daga (SC, 2014)

Structural Agnostic SpMV: Adapting CSR-Adaptive for Irregular Matrices. - Mayank Daga; Joseph L. Greathouse (HiPC, 2015)

Exploring Parallel Programming Models for Heterogeneous Computing Systems. - Mayank Daga; Zachary S. Tschirhart; Chip Freitag (IISWC, 2015)

On the Performance, Energy, and Power of Data-Access Methods in Heterogeneous Computing Systems. - Rubasri Kalidas; Mayank Daga; Konstantinos Krommydas; Wu-chun Feng (IPDPS Workshops, 2015)

Multiscale Approximation with Graphical Processing Units for Multiplicative Speedup in Molecular Dynamics. - Ramu Anandakrishnan; Mayank Daga; Alexey Onufriev; Wu-chun Feng (BCB, 2016)

clSPARSE: A Vendor-Optimized Open-Source Sparse BLAS Library. - Joseph L. Greathouse; Kent Knox; Jakub Pola; Kiran Varaganti; Mayank Daga (IWOCL, 2016)

Implementing directed acyclic graphs with the heterogeneous system architecture. - Sooraj Puthoor; Ashwin M. Aji; Shuai Che; Mayank Daga; Wei Wu 016; Bradford M. Beckmann; Gregory Rodgers (GPGPU@PPoPP, 2016)

On the Acceleration of Graph500: Characterizing PCIe Overheads with Multi-GPUs. - Mayank Daga (VECPAR, 2016)

MIOpen: An Open Source Library For Deep Learning Primitives. - Jehandad Khan; Paul Fultz; Artem Tamazov; Daniel Lowell; Chao Liu; Michael Melesse; Murali Nandhimandalam; Kamil Nasyrov; Ilya Perminov; Tejash Shah; Vasilii Filippov; Jing Zhang; et al. (CoRR, 2019)