Data-centric view of algorithms displayed in the ACES Vislab CDGC Research Applications Reduce Energy Requirements of Computer Programs Security Applications that Need Real-Time Analysis of Provenance Graphs Machine learning Mesh Generation Algorithms & Modeling Galois System Data-centric Programming Inexact Computing Funding: NSF, IBM, Intel, DARPA Research Staff: Andrew Lenharth Sreepathi Pai Graduate Students: 10 PhD Students from ICES, CS, and ECE Keshav Pingali Director Brief description of some of the research activities in PADAS: Fast algorithms for robust (i.e., automatic, black box) analysis for medical images. Typically, problem-specific algorithms Top left image: MRI FLAIR Image of patient with Grade IV glioma. Top right image: Estimated segmentatio to Grey matter, white matter, cerebrospinal fluid, and tumor Bottom image: Spatial frequency of adult glioblastoma with 3D surface rendering (top) and 2D multiplanar slices (bottom). These tumors tend to appear much more frequently in some brain regions, and this depends on age. The Statistical atlas was constructed with registration algorithms. Data-centric view of algorithms displayed in the ACES Vislab

Performance of Galois system: Galois Project - CDGC Goal: Make it easier for applications programmers to use heterogeneous parallel computers (CPUs, GPUs, FPGAs) Strategy: Raise abstraction level of programming Implement compilers and runtime systems to get efficient execution Focus: Irregular applications, particularly those involving large graphical models Application areas: graph analytics, machine-learning, mesh generation Software: Galois system Data-centric programming model implemented in C++ Used by BAE, HP, and university groups all over the world Funding: NSF, IBM, Intel, and other companies Roughly $5 million in past 10 years Performance of Galois system: 512-core SGI Ultraviolet

RIPE Project - CDGC Rapid Identification and Prevention of Exfiltration (RIPE) Goal: Use CDGC-developed Galois graph processing system for security applications that need real-time analysis of provenance graphs Identifying clusters in social networks Funded by DARPA Transparent Computing Program (2016-20) Sub-contractor to BAE Systems $5.5 million total, UT portion is $1 million

Proteus Project - CDGC Goal: Funded by DARPA BRASS program (2016-2020) Images can often be rendered inexactly to reduce energy requirements without losing semantic content Goal: Exploit inexact computing and machine learning to reduce energy requirements of programs Funded by DARPA BRASS program (2016-2020) Building Resource Adaptive Software Systems Joint project with Rice, MIT, and Chicago $8 million total, UT portion is $1.725 million