47 citations · 128 across the 12 of their papers we have counts for
13 papers
IRB: Automated Generation of Robust Factuality Benchmarks
Lam Thanh Do, Bhagyashree Taleka, Hozaifa Ammar Bhutta +3
Static benchmarks for RAG systems often suffer from rapid saturation and require significant manual effort to maintain robustness. To address this, we present IRB, a framework for…
Five-Minute Rule 40 Years Later: A First-Principles Revisit for Modern Memory Hierarchy
Tong Zhang, Vikram Sharma Mailthody, Fei Sun +7
In 1987, Jim Gray and Gianfranco Putzolu introduced the five-minute rule, a simple, storage-memory-economics-based heuristic for deciding when data should live in DRAM rather than…
Strata: Hierarchical Context Caching for Long Context Language Model Serving
Zhiqiang Xie, Ziyi Xu, Mark Zhao +5
Large Language Models (LLMs) with expanding context windows face significant performance hurdles. While caching key-value (KV) states is critical for avoiding redundant computation…
SSDTrain: An Activation Offloading Framework to SSDs for Faster Large Language Model Training
Kun Wu, Jeongmin Brian Park, Xiaofan Zhang +5
The growth rate of the GPU memory capacity has not been able to keep up with that of the size of large language models (LLMs), hindering the model training process. In particular,…
LSM-GNN: Large-scale Storage-based Multi-GPU GNN Training by Optimizing Data Transfer Scheme
Jeongmin Brian Park, Kun Wu, Vikram Sharma Mailthody +3
Graph Neural Networks (GNNs) are widely used today in recommendation systems, fraud detection, and node/link classification tasks. Real world GNNs continue to scale in size and req…
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…
IGB: Addressing The Gaps In Labeling, Features, Heterogeneity, and Size of Public Graph Datasets for Deep Learning Research
Arpandeep Khatua, Vikram Sharma Mailthody, Bhagyashree Taleka +3
Graph neural networks (GNNs) have shown high potential for a variety of real-world, challenging applications, but one of the major obstacles in GNN research is the lack of large-sc…
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…
Safer Illinois and RokWall: Privacy Preserving University Health Apps for COVID-19
Vikram Sharma Mailthody, James Wei, Nicholas Chen +14
COVID-19 has fundamentally disrupted the way we live. Government bodies, universities, and companies worldwide are rapidly developing technologies to combat the COVID-19 pandemic a…
Tearing Down the Memory Wall
Zaid Qureshi, Vikram Sharma Mailthody, Seung Won Min +3
We present a vision for the Erudite architecture that redefines the compute and memory abstractions such that memory bandwidth and capacity become first-class citizens along with c…
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…
EMOGI: Efficient Memory-access for Out-of-memory Graph-traversal In GPUs
Seung Won Min, Vikram Sharma Mailthody, Zaid Qureshi +3
Modern analytics and recommendation systems are increasingly based on graph data that capture the relations between entities being analyzed. Practical graphs come in huge sizes, of…