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MaxK-GNN: Extremely Fast GPU Kernel Design for Accelerating Graph Neural Networks Training.

, , , , , , , , and . ASPLOS (2), page 683-698. ACM, (2024)

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Scalability Limitations of Processing-in-Memory using Real System Evaluations., , , , , , , , , and 1 other author(s). Proc. ACM Meas. Anal. Comput. Syst., 8 (1): 5:1-5:28 (2024)Student cluster competition 2018, team northeastern university: Reproducing performance of a multi-physics simulations of the Tsunamigenic 2004 Sumatra Megathrust earthquake on the AMD EPYC 7551 architecture., , , , , , , and . Parallel Comput., (2019)JAXED: Reverse Engineering DNN Architectures Leveraging JIT GEMM Libraries., , , and . SEED, page 189-202. IEEE, (2021)Enabling Accelerators for Graph Computing.. CoRR, (2023)Accelerating Polynomial Multiplication for Homomorphic Encryption on GPUs., , , , , , , , and . SEED, page 61-72. IEEE, (2022)MaxK-GNN: Extremely Fast GPU Kernel Design for Accelerating Graph Neural Networks Training., , , , , , , , and . ASPLOS (2), page 683-698. ACM, (2024)Accelerating Finite Field Arithmetic for Homomorphic Encryption on GPUs., , , , , , , , and . IEEE Micro, 43 (5): 55-63 (September 2023)SMASH: Sparse Matrix Atomic Scratchpad Hashing.. CoRR, (2021)MaxK-GNN: Towards Theoretical Speed Limits for Accelerating Graph Neural Networks Training., , , , , , , , and . CoRR, (2023)GME: GPU-based Microarchitectural Extensions to Accelerate Homomorphic Encryption., , , , , , , , , and 2 other author(s). MICRO, page 670-684. ACM, (2023)