activity
20232026
most citedQuantifying the Performance Benefits of Partitioned Communication in MPI

7 citations · 20 across the 8 of their papers we have counts for

collaborators

8 papers

cs.DC2026

Implementing True MPI Sessions and Evaluating MPI Initialization Scalability

Hui Zhou, Kenneth Raffenetti, Yanfei Guo +2

Sessions is one of the major features introduced in the MPI-4 standard. It offers an alternative to the traditional world communicator model by allowing applications to construct c…

cs.DC2025★ 3 cited

FIRST: Federated Inference Resource Scheduling Toolkit for Scientific AI Model Access

Aditya Tanikanti, Benoit Côté, Yanfei Guo +9

We present the Federated Inference Resource Scheduling Toolkit (FIRST), a framework enabling Inference-as-a-Service across distributed High-Performance Computing (HPC) clusters. FI…

cs.DC2024★ 4 cited

Frustrated with MPI+Threads? Try MPIxThreads!

Hui Zhou, Ken Raffenetti, Junchao Zhang +2

MPI+Threads, embodied by the MPI/OpenMP hybrid programming model, is a parallel programming paradigm where threads are used for on-node shared-memory parallelization and MPI is use…

cs.DC2024★ 1 cited

Generating Bindings in MPICH

Hui Zhou, Ken Raffenetti, Wesley Bland +1

The MPI Forum has recently adopted a Python scripting engine for generating the API text in the standard document. As a by-product, it made available reliable and rich descriptions…

cs.DC2023★ 7 cited

Quantifying the Performance Benefits of Partitioned Communication in MPI

Thomas Gillis, Ken Raffenetti, Hui Zhou +2

Partitioned communication was introduced in MPI 4.0 as a user-friendly interface to support pipelined communication patterns, particularly common in the context of MPI+threads. It…

cs.DC2023

gZCCL: Compression-Accelerated Collective Communication Framework for GPU Clusters

Jiajun Huang, Sheng Di, Xiaodong Yu +11

GPU-aware collective communication has become a major bottleneck for modern computing platforms as GPU computing power rapidly rises. A traditional approach is to directly integrat…

cs.DC2023★ 2 cited

Accelerating MPI Collectives with Process-in-Process-based Multi-object Techniques

Jiajun Huang, Kaiming Ouyang, Yujia Zhai +8

In the exascale computing era, optimizing MPI collective performance in high-performance computing (HPC) applications is critical. Current algorithms face performance degradation d…

cs.DC2023★ 3 cited

An Optimized Error-controlled MPI Collective Framework Integrated with Lossy Compression

Jiajun Huang, Sheng Di, Xiaodong Yu +11

With the ever-increasing computing power of supercomputers and the growing scale of scientific applications, the efficiency of MPI collective communications turns out to be a criti…