Published in USENIX Annual Technical Conference (ATC), 2021
A runtime-based approach to predict computational performance for DNN training across different GPU hardware.
Recommended citation: Geoffrey X. Yu, Yubo Gao, Pavel Golikov, Gennady Pekhimenko. (2021). "Habitat: A Runtime-Based Computational Performance Predictor for Deep Neural Network Training." USENIX ATC 2021. pp. 503-521.
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Published in International Conference on Parallel Architectures and Compilation Techniques (PACT), 2022
A holistic approach to fine-grained GPU sharing that improves GPU utilization in cloud computing environments.
Recommended citation: Xiaodan Serina Tan, Pavel Golikov, Nandita Vijaykumar, Gennady Pekhimenko. (2022). "GPUPool: A Holistic Approach to Fine-Grained GPU Sharing in the Cloud." PACT 2022. pp. 317-332.
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Published in IEEE Computer Architecture Letters, 2026
FASED: A method for fusing addition and shift operations to compute dot products more efficiently in hardware.
Recommended citation: Pavel Golikov, Karthik Ganesan, Gennady Pekhimenko, Mark C. Jeffrey. (2026). "Fusing Adds and Shifts for Efficient Dot Products." IEEE Computer Architecture Letters. 25(1), pp. 33-36.
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