Picasso: Memory-Efficient Graph Coloring Using Palettes With Applications in Quantum Computing

Conference · Mon Jul 08 04:00:00 EDT 2024

DOI:https://doi.org/10.1109/IPDPS57955.2024.00029· OSTI ID:2426333

Ferdous, S M. ^[1]; Neff, Reece W. ^[1]; Peng, Bo ^[1]; Shuvo, Salman S. ^[1]; Minutoli, Marco ^[1]; Mukherjee, Sayak ^[1]; Kowalski, Karol ^[1]; Becchi, Michela ^[2]; Halappanavar, Mahantesh ^[1]

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1. BATTELLE (PACIFIC NW LAB)
2. North Carolina State University

A coloring of a graph is an assignment of colors to vertices such that no two neighboring vertices have the same color. The need for memory-efficient coloring algorithms is motivated by their application in computing clique partitions of graphs arising in quantum computations where the objective is to map a large set of Pauli strings into a compact set of unitaries. We present Picasso, a randomized memory-efficient iterative parallel graph coloring algorithm with theoretical sublinear space guarantees under practical assumptions. The parameters of our algorithm provide a trade-off between coloring quality and resource consumption. To assist the user, we also propose a machine learning model to predict the coloring algorithm’s parameters considering these trade-offs. We provide a sequential and a parallel implementation of the proposed algorithm. We perform an experimental evaluation on a 64-core AMD CPU equipped with 512 GB of memory and an Nvidia A100 GPU with 40GB of memory. For a small dataset where existing coloring algorithms can be executed within the 512 GB memory budget, we show up to 68× memory savings. On massive datasets we demonstrate that GPU-accelerated Picasso can process inputs with 49.5× more Pauli strings (vertex set in our graph) and 2,478× more edges than state-of-the-art parallel approaches.

• Research Organization: Pacific Northwest National Laboratory (PNNL), Richland, WA (United States)
• Sponsoring Organization: USDOE
• DOE Contract Number: AC05-76RL01830
• OSTI ID: 2426333
• Report Number(s): PNNL-SA-191110
• Country of Publication: United States
• Language: English

Related Subjects

artificial intelligence
quantum computing