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

Paper Published: (June 14, 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, [more...]

Paper Published: (May 18, 2020)

"Benanza: Automatic Benchmark Generation to Compute "Lower-bound" Latency and Inform Optimizations of Deep Learning Models on GPUs", Cheng Li, Abdul Dakkak, Jinjun Xiong, Wen-mei Hwu, IPDPS. [more...]

SC20 Student Cluster Reproducibility Committee Selects MemXCT: Memory-Centric X-ray CT Reconstruction with Massive Parallelization (April 15, 2020)

The SC20 Reproducibility Committee has selected the SC19 paper MemXCT: Memory-Centric X-ray CT Reconstruction with Massive Parallelization, to serve as the Student Cluster Competition (SCC) benchmark for the Reproducibility Challenge this year. The authors and the Reproducibility Committee have been working to create a reproducible benchmark that builds on the papers results. At SC20, the sixteen SCC teams will be asked to run the benchmark, replicating the findings from the original paper under different settings and with different datasets.

MemXCT: Memory-Centric X-ray CT Reconstruction with Massive Parallelization (PDF)

Omer Anjun Receives Best Paper Award for GPU Work with 3D Stencils (November 19, 2019)

CSL postdoc Omer Anjum, a member of the IMPACT group led by CSL Professor Wen-mei Hwu, recently wrote a paper on his work on high-order stencils titled An Efficient GPU Implementation Technique for Higher-Order 3D Stencils. The publication, which outlines a method of reusing data inside a GPU to improve bandwidth, received a Best Paper Award at the International Conference on High Performance Computing and Communications (HPCC).

Hwu extends GPU principles in general parallel computing applications (November 7, 2019)

The computations of modern hardware are so complex that it requires multiple processors to parallelize the task that is being performed. According to an article from Built In, Nvidia approached ECE ILLINOIS Professor Wen-mei Hwu, AMD Jerry Sanders Chair of Electrical and Computer Engineering, to help extend their designs with GPUs into general parallel computing applications. 

How Parallel Processing Solves Our Biggest Comoputational Problems (November 7, 2019)

Take all the help you can get.

If parallel computing has a central tenet, that might be it. Some of the crazy-complex computations asked of todays hardware are so demanding that the compute burden must be borne by multiple processors, effectively parallelizing whatever task is being performed. The result? Slashed latencies and turbocharged completion times.

Perhaps the most notable push toward parallelism happened around 2006, when tech hardware powerhouse Nvidia approached Wen-mei Hwu, a professor of electrical and computer engineering at the University of Illinois-Urbana Champaign. Nvidia was designing graphics processing units (GPUs) which, thanks to large numbers of threads and cores, had far higher memory bandwidth than the traditional central processing unit (CPUs) as a way to process huge numbers of pixels.

Student Innovation Award and Honorable Mentions at the IEEE HPEC Graph Challenge (September 25, 2019)

The IMPACT group graph challenge team (Omer Anjum, Carl Pearson, Mohammad Almasri, Sitao Huang, Vikram Mailthody, Zaid Qureshi, Professor Wen-Mei Hwu) and collaborators (Jinjun Xiong of IBM Watson Research, and Professor Rakesh Nagi of Illinois Industrial and Systems Engineering) received a student innovation award (led by Mohammad) and two honorable mentions (led by Carl and Sitao) at IEEE High Performance Extreme Computing 2019!!

Abdul Dakkak will be presenting D4P at the OpenPower Summit (August 19, 2019)

D4P: The Power Platform for Docker Online Container Authoring

The aim of D4P is to enrich the Power container ecosystem by providing both a platform for developers to create docker containers and for Power community to find docker images. Already, we have built and published over 200 docker images that are available in the D4P image catalog. User contribution is key to extending D4P's catalog. D4P is available online and slated to be the hub for the Power
community to create, discover, and use docker images.

IMPACT Group Win HPCC Best Paper Award! (August 10, 2019)

Omer Anjum, Simon Garcia de Gonzalo, Mert Hidayetoglu and Wen-mei Hwu have been awarded the best paper award for their work on "An Efficient GPU Implementation for Higher-Order 3D Stencils". Prof. Hwu presented the paper at the 21st IEEE International Conference on High-Performance Computing and Communications Conference.

Cheng Li and Abdul Dakkak will be presenting the "MLPerf-Bench: Benchmarking Deep Learning Systems" Tutorial at ISCA'19 (June 22, 2019)

Cheng Li and Abdul Dakkak will present MLModelScope again as part of the MLPerf Bench Tutorial at ISCA. This follows a successful packed room presentation of ASPLOS'19 in March.

The goal of the tutorial is to bring experts from the industry and academia together to shed light on the following topics to foster systematic development, reproducible evaluation, and performance analysis of deep learning artifacts. It seeks to address the following questions:
  1. What are the benchmarks that can effectively capture the scope of the ML/DL domain?
  2. Are the existing frameworks sufficient for this purpose?
  3. What are some of the industry-standard evaluation platforms or harnesses?
  4. What are the metrics for carrying out an effective comparative evaluation?

(View Archive of Highlighted Items)