TY - GEN
T1 - Scalable Block-Sparse Matrix Multiplication Using Template Task Graphs
AU - Schuchart, Joseph
AU - Bouteiller, Aurelien
AU - Herault, Thomas
AU - Valeev, Edvard
AU - Bosilca, George
AU - Harrison, Robert J.
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2026.
PY - 2026
Y1 - 2026
N2 - Block-sparse matrix operations are a special case of general sparse algebra where the matrix is sparsely populated with dense blocks, e.g., in sparse tensor algebra for quantum chemistry. One of the challenges of implementing distributed matrix multiplication C=A×B in general is the management of communication flows since both input matrices A and B are readily available and must be distributed to the processes computing the relevant blocks of C. In this paper, we propose an addition to the Template Task Graph programming model that allows applications to constrain the execution of tasks using a flexible API. We show that such constraints can be used in a pure dataflow model to replace artificial control flow with a more structured approach. In the context of sparse matrix multiplication, we found that constraints allow us to limit the number of concurrent communications and thus avoid creating a bottleneck in the network.
AB - Block-sparse matrix operations are a special case of general sparse algebra where the matrix is sparsely populated with dense blocks, e.g., in sparse tensor algebra for quantum chemistry. One of the challenges of implementing distributed matrix multiplication C=A×B in general is the management of communication flows since both input matrices A and B are readily available and must be distributed to the processes computing the relevant blocks of C. In this paper, we propose an addition to the Template Task Graph programming model that allows applications to constrain the execution of tasks using a flexible API. We show that such constraints can be used in a pure dataflow model to replace artificial control flow with a more structured approach. In the context of sparse matrix multiplication, we found that constraints allow us to limit the number of concurrent communications and thus avoid creating a bottleneck in the network.
KW - Scheduling Constraints
KW - Sparse Matrix Multiplication
KW - Template Task Graph
UR - https://www.scopus.com/pages/publications/105019055625
U2 - 10.1007/978-3-031-97196-9_10
DO - 10.1007/978-3-031-97196-9_10
M3 - Conference contribution
AN - SCOPUS:105019055625
SN - 9783031971952
T3 - Lecture Notes in Computer Science
SP - 120
EP - 132
BT - Asynchronous Many-Task Systems and Applications - 3rd International Workshop, WAMTA 2025, Proceedings
A2 - Diehl, Patrick
A2 - Cao, Qinglei
A2 - Herault, Thomas
A2 - Bosilca, George
PB - Springer Science and Business Media Deutschland GmbH
T2 - 3rd International Workshop on Asynchronous Many-Task Systems and Applications, WAMTA 2025
Y2 - 19 February 2025 through 21 February 2025
ER -