305 citations · 311 across the 9 of their papers we have counts for
9 papers
MSREP: A Fast yet Light Sparse Matrix Framework for Multi-GPU Systems
Jieyang Chen, Chenhao Xie, Jesun S Firoz +5
Sparse linear algebra kernels play a critical role in numerous applications, covering from exascale scientific simulation to large-scale data analytics. Offloading linear algebra k…
Spy in the GPU-box: Covert and Side Channel Attacks on Multi-GPU Systems
Sankha Baran Dutta, Hoda Naghibijouybari, Arjun Gupta +3
The deep learning revolution has been enabled in large part by GPUs, and more recently accelerators, which make it possible to carry out computationally demanding training and infe…
Bit-GraphBLAS: Bit-Level Optimizations of Matrix-Centric Graph Processing on GPU
Jou-An Chen, Hsin-Hsuan Sung, Xipeng Shen +3
In a general graph data structure like an adjacency matrix, when edges are homogeneous, the connectivity of two nodes can be sufficiently represented using a single bit. This insig…
Fast and Scalable Sparse Triangular Solver for Multi-GPU Based HPC Architectures
Chenhao Xie, Jieyang Chen, Jesun S Firoz +5
Designing efficient and scalable sparse linear algebra kernels on modern multi-GPU based HPC systems is a daunting task due to significant irregular memory references and workload…
Leaky Buddies: Cross-Component Covert Channels on Integrated CPU-GPU Systems
Sankha Baran Dutta, Hoda Naghibijouybari, Nael Abu-Ghazaleh +2
Graphics Processing Units (GPUs) are a ubiquitous component across the range of today's computing platforms, from phones and tablets, through personal computers, to high-end server…
ARENA: Asynchronous Reconfigurable Accelerator Ring to Enable Data-Centric Parallel Computing
Cheng Tan, Chenhao Xie, Tong Geng +4
The next generation HPC and data centers are likely to be reconfigurable and data-centric due to the trend of hardware specialization and the emergence of data-driven applications.…