Graph Pattern Mining (GPMI) applications are considered a new class of data-intensive applications -- they generate massive irregular computation workloads and pose memory access challenges, which degrade the performance and scalability significantly. Researchers at the Illinois Institute of Technology approach the problem by using the emerging process-in-memory architecture.
Interested in protecting remote devices from malicious actors? Learn about how a researcher at the University of Missouri is approaching this problem with genetic algorithms and host fingerprinting! Also included is a YouTube video where Dr. Aksoy discusses this research.
Learn how researchers are pairing autonomous vehicles with Chameleon to bridge edge to cloud computation to conduct marine surveys. Featuring work presented at the 2021 Supercomputing conference, with a notebook available on Trovi that you can reproduce yourself, and a YouTube video to accompany it!
Learn about how researchers at the University of Chicago are using Chameleon’s new edge computing testbed, CHI@Edge to investigate how resource management can be applied to the concept of shared edge to optimize AI applications.
Learn about how researchers combine ARA, an at-scale platform for advanced wireless research and CHI@Edge as a baseline software platform for agriculture applications including high-throughput crop phenotyping, precision livestock farming, agriculture automation and rural education.
Learn about IndySCC, a virtual version of SuperComputing's Student Cluster Competition (SCC) which was conducted on Chameleon! Learn all about the competition, how it was run with Chameleon, and of course, the winners!
Learn all about extending Pegasus, a fully featured workflow management system, to edge devices in one of the first examples of research done on CHI@Edge, Chameleon's edge computing testbed.
Researchers at the University of Missouri explore novel middle-man data poisoning attacks with facial authentication applications and propose a defense architecture. Learn more about these attacks, their solution, and the researchers themselves in November's featured user experiments blog.
Learn about University of Chicago PhD student Chengcheng Wan's research on designing a solution to integrate machine learning components into traditional software systems to maximize energy efficiency, latency, and accuracy constraints at machine learning, system and application levels, with related research published at USENIX ATC, ICML, and ICSE.
University of Texas, San Antonio Professor Wei Wang and PhD student Sen He investigate performance testing for cloud computing research to help make your research more efficient and cost effective. Learn about their research, which won the ACM SIGSOFT Distinguished Paper award in 2019, experience on Chameleon and AWS, and life philosophies.