The International Conference for High Performance Computing, Networking, Storage, and Analysis
The International Conference for High Performance Computing, Networking, Storage, and Analysis
Workshop: The 8th Annual Parallel Applications Workshop: Alternatives To MPI+X (PAW-ATM 2025)
Authors: Alexander Fell, Yuqing Wang, Tianshuo Su, Marziyeh Nourian, and Wenyi Wang (University of Chicago); Jose M. Monsalve-Diaz (Argonne National Laboratory (ANL)); Andronicus Samsundar Rajasukumar, Jiya Su, Ruiqi Xu, Rajat Khandelwal, and Tianchi Zhang (University of Chicago); David F. Gleich (Purdue University); and Yanjing Li, Hank Hoffmann, and Andrew A. Chien (University of Chicago)
Abstract: Programming irregular graph applications is challenging on today's scalable supercomputers.
We describe a novel programming model, KVMSR+UDWeave, that supports extreme scaling by exposing fine-grained parallelism. By enabling the expression of maximum parallelism, it opens the door for extreme scaling, even on both small and large graph problems.
KVMSR+UDWeave cleanly separates the three key dimensions of parallel programming: parallelism, computation binding, and data placement. This decomposition reduces effort to achieve scalable, high-performance for graph algorithms on real-world, highly skewed graphs. Key features of the UpDown supercomputer (computation location naming and shared global address space) enable decomposition and scalable, high performance.
In the IARPA AGILE program, we built numerous graph benchmarks and workflows, and use them to illustrate the programming model. Simulation results for UpDown show excellent strong-scaling to million-fold hardware parallelism and high absolute performance. Results suggest KVMSR+UDWeave enables reduced programming effort for scaling the most demanding irregular applications.
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