26 citations · 135 across the 22 of their papers we have counts for
10 papers · 1 filter
CODAG: Characterizing and Optimizing Decompression Algorithms for GPUs
Jeongmin Park, Zaid Qureshi, Vikram Mailthody +10
Data compression and decompression have become vital components of big-data applications to manage the exponential growth in the amount of data collected and stored. Furthermore, b…
Accelerating Sampling and Aggregation Operations in GNN Frameworks with GPU Initiated Direct Storage Accesses
Jeongmin Brian Park, Vikram Sharma Mailthody, Zaid Qureshi +1
Graph Neural Networks (GNNs) are emerging as a powerful tool for learning from graph-structured data and performing sophisticated inference tasks in various application domains. Al…
A Compiler Framework for Optimizing Dynamic Parallelism on GPUs
Mhd Ghaith Olabi, Juan Gómez Luna, Onur Mutlu +2
Dynamic parallelism on GPUs allows GPU threads to dynamically launch other GPU threads. It is useful in applications with nested parallelism, particularly where the amount of neste…
TEMPI: An Interposed MPI Library with a Canonical Representation of CUDA-aware Datatypes
Carl Pearson, Kun Wu, I-Hsin Chung +2
MPI derived datatypes are an abstraction that simplifies handling of non-contiguous data in MPI applications. These datatypes are recursively constructed at runtime from primitive…
Petascale XCT: 3D Image Reconstruction with Hierarchical Communications on Multi-GPU Nodes
Mert Hidayetoglu, Tekin Bicer, Simon Garcia de Gonzalo +6
X-ray computed tomography is a commonly used technique for noninvasive imaging at synchrotron facilities. Iterative tomographic reconstruction algorithms are often preferred for re…
At-Scale Sparse Deep Neural Network Inference with Efficient GPU Implementation
Mert Hidayetoglu, Carl Pearson, Vikram Sharma Mailthody +4
This paper presents GPU performance optimization and scaling results for inference models of the Sparse Deep Neural Network Challenge 2020. Demands for network quality have increas…