102 citations · 196 across the 10 of their papers we have counts for
10 papers
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…
Understanding the Effect of the Long Tail on Neural Network Compression
Harvey Dam, Vinu Joseph, Aditya Bhaskara +3
Network compression is now a mature sub-field of neural network research: over the last decade, significant progress has been made towards reducing the size of models and speeding…
ArctyrEX : Accelerated Encrypted Execution of General-Purpose Applications
Charles Gouert, Vinu Joseph, Steven Dalton +3
Fully Homomorphic Encryption (FHE) is a cryptographic method that guarantees the privacy and security of user data during computation. FHE algorithms can perform unlimited arithmet…
Stream-K: Work-centric Parallel Decomposition for Dense Matrix-Matrix Multiplication on the GPU
Muhammad Osama, Duane Merrill, Cris Cecka +2
We introduce Stream-K, a work-centric parallelization of matrix multiplication (GEMM) and related computations in dense linear algebra. Whereas contemporary decompositions are prim…
Efficient Sparsely Activated Transformers
Salar Latifi, Saurav Muralidharan, Michael Garland
Transformer-based neural networks have achieved state-of-the-art task performance in a number of machine learning domains including natural language processing and computer vision.…
GPU-Initiated On-Demand High-Throughput Storage Access in the BaM System Architecture
Zaid Qureshi, Vikram Sharma Mailthody, Isaac Gelado +10
Graphics Processing Units (GPUs) have traditionally relied on the host CPU to initiate access to the data storage. This approach is well-suited for GPU applications with known data…
Going Beyond Classification Accuracy Metrics in Model Compression
Vinu Joseph, Shoaib Ahmed Siddiqui, Aditya Bhaskara +5
With the rise in edge-computing devices, there has been an increasing demand to deploy energy and resource-efficient models. A large body of research has been devoted to developing…
A Programmable Approach to Neural Network Compression
Vinu Joseph, Saurav Muralidharan, Animesh Garg +2
Deep neural networks (DNNs) frequently contain far more weights, represented at a higher precision, than are required for the specific task which they are trained to perform. Conse…
Accelerating Reinforcement Learning through GPU Atari Emulation
Steven Dalton, Iuri Frosio, Michael Garland
We introduce CuLE (CUDA Learning Environment), a CUDA port of the Atari Learning Environment (ALE) which is used for the development of deep reinforcement algorithms. CuLE overcome…
AdaBatch: Adaptive Batch Sizes for Training Deep Neural Networks
Aditya Devarakonda, Maxim Naumov, Michael Garland
Training deep neural networks with Stochastic Gradient Descent, or its variants, requires careful choice of both learning rate and batch size. While smaller batch sizes generally c…