Welcome to the Parallel Architecture, System, and Algorithm Lab
Campus Photo
University of California, Merced
Parallel Architecture, System, and Algorithm (PASA) Lab at the Electrical Engineering and Computer Science, University of California, Merced performs research in core technologies for large-scale parallel systems. The core theme of our research is to study how to enable scalable and efficient execution of enterprise and scientific applications on increasingly complex large-scale parallel systems. Our work creates innovation in runtime, architecture, performance modeling, and programming models; We also investigate the impact of novel architectures (e.g., non-volatile memory and accelerator with massive parallelism) on the designs of applications and runtime. Our goal is to improve the performance, reliability, energy efficiency, and productivity of large-scale parallel systems.
See our Research and Publications pages for more information about our work. For information about our group members, see our People page.
[9/2020] A paper “HM-ANN: Efficient Billion-Point Nearest Neighbor Search on Heterogeneous Memory” is accepted in NeurIPS'20!
[9/2020] Congratulate Jiawen for his internship in the Facebook research!
[8/2020] The lab has a new website! :)
[8/2020] A paper “MATCH: An MPI Fault Tolerance Benchmark Suite” is accepted in IISWC'20.
[7/2020] A paper “Exploring Non-Volatility of Non-Volatile Memory for High Performance Computing Under Failures” is accepted in Cluster’20.
[6/2020] A paper “Ribbon: High Performance Cache Line Flushing for Persistent Memory” is accepted in PACT’20.
[6/2020] A paper “Smart-PGSim: Using Neural Network to Accelerate AC-OPF Power Grid Simulation” is accepted in SC’20.
[3/2020] Congratulations to Jie Ren, Kai, Jie Liu, Jiawen and Wenqian for their summer internships in Microsoft research, ByteDance, Futurewei and PNNL!
[3/2020] A paper “RIANN: Real-time Incremental Learning with Approximate Nearest Neighbor on Mobile Devices” is accepted in USENIX OpML’20.
[2/2020] A paper “Flame: A Self-Adaptive Auto-Labeling System for Heterogeneous Mobile Processors” is accepted in On-Device Intelligence Workshop at MLSys’20.
[1/2020] Dong was invited to join IEEE Transactions on Parallel and Distributed Systems (TPDS) Review Board.