1 citations · 1 across the 8 of their papers we have counts for
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OptiML: An End-to-End Framework for Program Synthesis and CUDA Kernel Optimization
Arijit Bhattacharjee, Heng Ping, Son Vu Le +3
Generating high-performance CUDA kernels remains challenging due to the need to navigate a combinatorial space of low-level transformations under noisy and expensive hardware feedb…
HGFM: Towards unifying Homogeneity and Heterogeneity on Text-Attributed Graphs
Trung-Kien Nguyen, Heng Ping, Shixuan Li +4
The growing interests and applications of graph learning in diverse domains have propelled the development of a unified model generalizing well across different graphs and tasks, k…
A Structure-Aware Framework for Learning Device Placements on Computation Graphs
Shukai Duan, Heng Ping, Nikos Kanakaris +9
Computation graphs are Directed Acyclic Graphs (DAGs) where the nodes correspond to mathematical operations and are used widely as abstractions in optimizations of neural networks.…
Unlocking Deep Learning: A BP-Free Approach for Parallel Block-Wise Training of Neural Networks
Anzhe Cheng, Zhenkun Wang, Chenzhong Yin +5
Backpropagation (BP) has been a successful optimization technique for deep learning models. However, its limitations, such as backward- and update-locking, and its biological impla…
PerfRL: A Small Language Model Framework for Efficient Code Optimization
Shukai Duan, Nikos Kanakaris, Xiongye Xiao +8
Code optimization is a challenging task requiring a substantial level of expertise from developers. Nonetheless, this level of human capacity is not sufficient considering the rapi…