The objective of IMPACT (Illinois Microarchitecture Project using Algorithms and Compiler Technology) is to provide critical research, architecture innovation, and algorithm and compiler prototypes for heterogeneous parallel architectures. We achieve portable performance and energy efficiency for emerging real-world applications by developing novel hardware, compiler, and algorithmic solutions.
 

 

Recent & Highlighted Items

"PyTorch-Direct: Enabling GPU Centric Data Access for Very Large Graph Neural Network Training with Irregular Accesses", Seung Won Min, Kun Wu, Sitao Huang, Mert Hidayetoglu, Jinjun Xiong, Eiman Ebrahimi, Deming Chen, Wen-mei Hwu, https://arxiv.org/abs/2101.07956. [more...]

Paper Published: (November 16, 2020)

"Petascale XCT: 3D Image Reconstruction with Hierarchical Communications on Multi-GPU Nodes", Mert Hidayetoglu, Tekin Bicer, Simon Garcia de Gonzalo, Bin Ren, Vincent De Andrade, Doga Gursoy, Rajkumar Kettimuthu, Ian Foster, Wen-mei Hwu, International Conference for High Performance Computing, Networking, Storage and Analysis (SC20). (Best Paper Award) [more...]

Paper Published: (October 26, 2020)

"EMOGI: Efficient Memory-access for Out-of-memory Graph-traversal in GPUs", Seung Won Min, Vikram Sharma Mailthody, Zaid Qureshi, Jinjun Xiong, Eiman Ebrahimi, Wen-mei Hwu, PVLDB, Volume 14, No. 2, October 2020. [more...]

Paper Accepted: (August 26, 2020)

"At-Scale Sparse Deep Neural Network Inference with Efficient GPU Implementation", Mert Hidayetoglu, Carl Pearson, Vikram Sharma Mailthody, Eiman Ebrahimi, Jinjun Xiong, Rakesh Nagi, Wen-mei Hwu, 2020 IEEE High Performance Extreme Computing Conference, HPEC 2020. (Graph Challenge Champion) [more...]

Paper Published: (January 20, 2021)

(View Archive of Highlighted Items)