Georgia Tech’s latest parallel computing research is being presented at the SIAM Conference on Parallel Processing for Scientific Computing (SIAM PP18), at Waseda University in Tokyo this week, March 7-10. School of Computational Science and Engineering (CSE) Associate Professor Rich Vuduc is serving as the current chair of the SIAM Activity Group on Supercomputing, which is co-sponsoring the SIAM PP conference alongside the Japan Society for Industrial and Applied Mathematics this year.
A large number of CSE faculty members and Ph.D. students have research accepted for this year’s program and will be presenting during the week’s meetings. Of the 17 research presentations from Georgia Tech at the conference, 13 include presentations and work by CSE researchers.
Parallel computing allows for larger problems to be divided into smaller ones, which then can be solved simultaneously. This type of computation is inherent to high-performance computing (HPC), but is gaining more notice in other areas of computing research because they are increasingly able to take advantage of frequency scaling, a computer architecture technique used to conserve power and reduce heat generated by the chip.
"Parallel processing continues to have an increasingly big impact on physical simulation, data analytics, and artificial intelligence. The talks by Georgia Tech researchers reflect this with their particular focus on how to fundamentally advance the algorithms, data structures, and numerical methods underlying these domains,” said Vuduc.
Some of the themes emerging in this year’s work from Georgia Tech researchers demonstrate a range of parallel processing abilities and applications that stem from four main areas: data and HPC, novel systems, algorithms and libraries, and large-scale simulations.
Data and HPC
Algorithms and Libraries
The following sessions include presentations by CSE faculty and Ph.D. students.
MS9: Tensor Decomposition for High Performance Data Analytics – Part I of III
HiCOO Hierarchical Storage of Sparse Tensors
Jiajia Li, Jimeng Sun, Rich Vuduc
MS20: Tensor Decomposition for High Performance Data Analytics – Part II of III
MS34: Architecture-Aware Graph Analytics – Part I of II
Scalable Graph Alignment on Modern Architectures
Ümit V. Çatalyürek, Bora Ucar, Abdurrahman Yasar
MS38: Deep Learning from HPC Perspective: Opportunities and Challenges – Part II of II
Faster, Smaller, and More Energy-Efficient Inference Using Codebook-based Quantization and FPGAs
Mikhail Isaev, Jeffrey Young, Rich Vuduc
MS41: Performance Engineering from the Node level to the Extreme Scale – Part II of II
Designing an Algorithm with a Tuning Knob that Controls its Power Consumption
Sara Karamati, Jeffrey Young, Rich Vuduc
MS56: Tensor Decomposition for High Performance Data Analytics – Part III of III
MS58: Scalable and Dynamic Graph Algorithms
Graph Analysis: New Algorithm Models, New Architectures
David A. Bader, Oded Green, Jason Riedy
MS68: Challenges in Parallel Adaptive Mesh Refinement – Part 1 of III
Structured and Unstructured Adaptivity in PETSc
MS73: Theory Meets Practice for High Performance Computing – Part II of II
Faster Parallel Tensor Compression using Randomization
MS87: Innovative Methods for High Performance Iterative Solvers – Part I of II
ParILUT – A New Parallel Threshold ILU
MS97: Emerging Architectural Support for Scientific Kernels – Part I of II
Sparse Tensor Decomposition on EMU Platform
Srinivas Eswar, Jiajia Li, Richard Vuduc, Patrick Lavin, Jeffrey Young
MS102: Large-Scale Simulation in Geodynamics – Part I of II
Thermal Inversion in Suduction Zones
MS109: Techniques for Developing Massively-Parallel Linear Solvers – Part I of II
ACHILES: An Asynchronous Iterative Sparse Linear Solver
The following sessions include presentations by Georgia Tech faculty and Ph.D. students.
MS14: On Batched BLAS Standardization – Part II of II
Batched DGEMM Operations in Density Matrix Renormalization Group
MS30: Resilience for Extreme Scale Computing – Part III of IV
Resilience with Asynchronous Many Task (AMT) Programming Models
MS50: Parallel Simulations in Life Sciences
Meshfree Simulations of Complex Flows Using General Finite Differences
Yaroslav Vasyliv and Alexander Alexeev