apache/systemds
Mar 2026 – May 2026
2 Months active
Languages Used
Java
Technical Skills
Javamatrix operationsperformance optimizationalgorithm optimizationdynamic programming
Elman Jahangiri
PROFILE
Elman Jahangiri
Over a two-month period, contributed to the apache/systemds repository by developing advanced matrix multiplication optimizations in Java. Delivered a performance-optimized Dense Matrix Multiply Kernel that eliminates explicit transpose steps, enabling in-place and tiled-transposition for common transposed-input patterns and improving both runtime and memory efficiency. Subsequently, implemented a dynamic programming-based optimizer for matrix multiplication chains involving transposes, replacing a heuristic approach with a cost-minimal execution plan using memoization. These enhancements were validated through automated regression and DML tests, demonstrating reduced computational costs and improved analytics workload performance. The work focused on algorithm optimization, dynamic programming, and matrix operations.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
2Total
Bugs
0
Commits
2
Features
2
Lines of code
2,050
Activity Months2
Your Network
193 peopleShared Repositories
57
Work History
May 2026
1 Commits • 1 Features
May 1, 2026May 2026 performance summary for apache/systemds: Delivered a dynamic programming (DP) based optimization for matrix multiplication chains that include transposes, replacing the previous heuristic approach. Introduced a new HOP rewrite rule to compute the optimal execution plan for chained multiplications, including transpositions. Implemented a DP algorithm with a memoization table to evaluate plans with and without transposes, validated by a suite of 24 automated DML tests asserting intermediate HOP dimensions and optimal parenthesization. The work closes issue #2465 and is backed by a focused commit (b7480917b5178b1f566f1c5aa68cfddaeb5e4f80).
1 Commits • 1 Features
May 1, 2026May 2026 performance summary for apache/systemds: Delivered a dynamic programming (DP) based optimization for matrix multiplication chains that include transposes, replacing the previous heuristic approach. Introduced a new HOP rewrite rule to compute the optimal execution plan for chained multiplications, including transpositions. Implemented a DP algorithm with a memoization table to evaluate plans with and without transposes, validated by a suite of 24 automated DML tests asserting intermediate HOP dimensions and optimal parenthesization. The work closes issue #2465 and is backed by a focused commit (b7480917b5178b1f566f1c5aa68cfddaeb5e4f80).
May 2026
March 2026
1 Commits • 1 Features
Mar 1, 2026March 2026 (2026-03) monthly summary for the apache/systemds repository. Delivered a performance-optimized Dense Matrix Multiply Kernel for transposed inputs, eliminating the need for explicit transpose steps and enabling in-place or tiled-transposition. This change significantly improves runtime and memory efficiency for common transposed-input matmul patterns (t(A)%*%B, A%*%t(B), t(A)%*%t(B)), accelerating analytics workloads.
March 2026
1 Commits • 1 Features
Mar 1, 2026March 2026 (2026-03) monthly summary for the apache/systemds repository. Delivered a performance-optimized Dense Matrix Multiply Kernel for transposed inputs, eliminating the need for explicit transpose steps and enabling in-place or tiled-transposition. This change significantly improves runtime and memory efficiency for common transposed-input matmul patterns (t(A)%*%B, A%*%t(B), t(A)%*%t(B)), accelerating analytics workloads.
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Quality Metrics
Correctness100.0%
Maintainability80.0%
Architecture100.0%
Performance100.0%
AI Usage20.0%
Skills & Technologies
Programming Languages
Java
Technical Skills
Javaalgorithm optimizationdynamic programmingmatrix operationsperformance optimization
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