109 citations · 173 across the 9 of their papers we have counts for
6 papers · 1 filter
Evaluating the Potential of Disaggregated Memory Systems for HPC applications
Nan Ding, Pieter Maris, Hai Ah Nam +8
Disaggregated memory is a promising approach that addresses the limitations of traditional memory architectures by enabling memory to be decoupled from compute nodes and shared acr…
Understanding the Impact of Input Entropy on FPU, CPU, and GPU Power
Sridutt Bhalachandra, Brian Austin, Samuel Williams +1
Power is increasingly becoming a limiting resource in high-performance, GPU-accelerated computing systems. Understanding the range and sources of power variation is essential in se…
Time-Based Roofline for Deep Learning Performance Analysis
Yunsong Wang, Charlene Yang, Steven Farrell +3
Deep learning applications are usually very compute-intensive and require a long run time for training and inference. This has been tackled by researchers from both hardware and so…
Hierarchical Roofline Performance Analysis for Deep Learning Applications
Charlene Yang, Yunsong Wang, Steven Farrell +2
This paper presents a practical methodology for collecting performance data necessary to conduct hierarchical Roofline analysis on NVIDIA GPUs. It discusses the extension of the Em…
Modern Gyrokinetic Particle-In-Cell Simulation of Fusion Plasmas on Top Supercomputers
Bei Wang, Stephane Ethier, William Tang +4
The Gyrokinetic Toroidal Code at Princeton (GTC-P) is a highly scalable and portable particle-in-cell (PIC) code. It solves the 5D Vlasov-Poisson equation featuring efficient utili…
Exploiting Multiple Levels of Parallelism in Sparse Matrix-Matrix Multiplication
Ariful Azad, Grey Ballard, Aydin Buluc +5
Sparse matrix-matrix multiplication (or SpGEMM) is a key primitive for many high-performance graph algorithms as well as for some linear solvers, such as algebraic multigrid. The s…