16 citations · 16 across the 2 of their papers we have counts for
3 papers
cs.DC2024
Scalable Dual Coordinate Descent for Kernel Methods
Zishan Shao, Aditya Devarakonda
Dual Coordinate Descent (DCD) and Block Dual Coordinate Descent (BDCD) are important iterative methods for solving convex optimization problems. In this work, we develop scalable D…
cs.DC2023
Sequential and Shared-Memory Parallel Algorithms for Partitioned Local Depths
Aditya Devarakonda, Grey Ballard
In this work, we design, analyze, and optimize sequential and shared-memory parallel algorithms for partitioned local depths (PaLD). Given a set of data points and pairwise distanc…
cs.DC2016★ 16 cited
Matrix Factorization at Scale: a Comparison of Scientific Data Analytics in Spark and C+MPI Using Three Case Studies
Alex Gittens, Aditya Devarakonda, Evan Racah +14
We explore the trade-offs of performing linear algebra using Apache Spark, compared to traditional C and MPI implementations on HPC platforms. Spark is designed for data analytics…