activity
20132026
most citedPerformance Evaluation of Python Parallel Programming Models: Charm4Py and mpi4py

13 citations · 38 across the 10 of their papers we have counts for

collaborators

12 papers

cs.DC2026

Communication-Aware Diffusion Load Balancing for Persistently Interacting Objects

Maya Taylor, Kavitha Chandrasekar, Laxmikant V. Kale

Parallel applications with irregular and time-varying workloads often suffer from load imbalance. Dynamic load balancing techniques address this challenge by redistributing work du…

cs.DC2024

CkIO: Parallel File Input for Over-Decomposed Task-Based Systems

Mathew Jacob, Maya Taylor, Laxmikant Kale

Parallel input performance issues are often neglected in large scale parallel applications in Computational Science and Engineering. Traditionally, there has been less focus on inp…

cs.DC2024

Shared Memory-Aware Latency-Sensitive Message Aggregation for Fine-Grained Communication

Kavitha Chandrasekar, Laxmikant Kale

Message aggregation is often used with a goal to reduce communication cost in HPC applications. The difference in the order of overhead of sending a message and cost of per byte tr…

cs.DC2022★ 12 cited

Quantifying Overheads in Charm++ and HPX using Task Bench

Nanmiao Wu, Ioannis Gonidelis, Simeng Liu +6

Asynchronous Many-Task (AMT) runtime systems take advantage of multi-core architectures with light-weight threads, asynchronous executions, and smart scheduling. In this paper, we…

cs.DC2022

Improving Scalability with GPU-Aware Asynchronous Tasks

Jaemin Choi, David F. Richards, Laxmikant V. Kale

Asynchronous tasks, when created with over-decomposition, enable automatic computation-communication overlap which can substantially improve performance and scalability. This is no…

cs.DC2021★ 13 cited

Performance Evaluation of Python Parallel Programming Models: Charm4Py and mpi4py

Zane Fink, Simeng Liu, Jaemin Choi +2

Python is rapidly becoming the lingua franca of machine learning and scientific computing. With the broad use of frameworks such as Numpy, SciPy, and TensorFlow, scientific computi…

cs.DC2021

Accelerating Communication for Parallel Programming Models on GPU Systems

Jaemin Choi, Zane Fink, Sam White +3

As an increasing number of leadership-class systems embrace GPU accelerators in the race towards exascale, efficient communication of GPU data is becoming one of the most critical…

physics.comp-ph2018★ 12 cited

Scalable GW software for quasiparticle properties using OpenAtom

Minjung Kim, Subhasish Mandal, Eric Mikida +7

The GW method, which can describe accurately electronic excitations, is one of the most widely used ab initio electronic structure technique and allows the physics of both molecula…

cs.DC2018

Histogram Sort with Sampling

Vipul Harsh, Laxmikant Kale, Edgar Solomonik

To minimize data movement, state-of-the-art parallel sorting algorithms use techniques based on sampling and histogramming to partition keys prior to redistribution. Sampling enabl…

astro-ph.IM2014★ 1 cited

Adaptive Techniques for Clustered N-Body Cosmological Simulations

Harshitha Menon, Lukasz Wesolowski, Gengbin Zheng +4

ChaNGa is an N-body cosmology simulation application implemented using Charm++. In this paper, we present the parallel design of ChaNGa and address many challenges arising due to t…

math.NA2014

Solvers for Electronic Structure in the Strong Scaling Limit

Nicolas Bock, Matt Challacombe, Laxmikant V. Kalé

We present a hybrid OpenMP/Charm++ framework for solving the Self-Consistent-Field eigenvalue problem with parallelism in the strong scaling regime, , wh…

cs.DC2013

Dynamic Load Balancing in GPU-Based Systems - Early Experiments

Alvaro Luiz Fazenda, Celso L. Mendes, Laxmikant V. Kale +2

The dynamic load-balancing framework in Charm++/AMPI, developed at the University of Illinois, is based on using processor virtualization to allow thread migration across processor…