1 citations · 2 across the 8 of their papers we have counts for
8 papers
Design Space Exploration of DMA based Finer-Grain Compute Communication Overlap
Shagnik Pal, Shaizeen Aga, Suchita Pati +2
Modern ML workloads demand distributing training and inference across multiple GPUs. However, these parallelization techniques often suffer from exposed critical-path communication…
DMA-Latte: Expanding the Reach of DMA Offloads to Latency-bound ML Communication
Suchita Pati, Shaizeen Aga, Mahzabeen Islam +3
Offloading communication to existing direct memory access (DMA) engines, available on most state-of-the-art commercial GPUs, has emerged as a practical and low-cost solution to eff…
Optimizing ML Concurrent Computation and Communication with GPU DMA Engines
Anirudha Agrawal, Shaizeen Aga, Suchita Pati +1
Concurrent computation and communication (C3) is a pervasive paradigm in ML and other domains, making its performance optimization crucial. In this paper, we carefully characterize…
Balanced Data Placement for GEMV Acceleration with Processing-In-Memory
Mohamed Assem Ibrahim, Mahzabeen Islam, Shaizeen Aga
With unprecedented demand for generative AI (GenAI) inference, acceleration of primitives that dominate GenAI such as general matrix-vector multiplication (GEMV) is receiving consi…
T3: Transparent Tracking & Triggering for Fine-grained Overlap of Compute & Collectives
Suchita Pati, Shaizeen Aga, Mahzabeen Islam +2
Large Language Models increasingly rely on distributed techniques for their training and inference. These techniques require communication across devices which can reduce scaling e…
Just-in-time Quantization with Processing-In-Memory for Efficient ML Training
Mohamed Assem Ibrahim, Shaizeen Aga, Ada Li +2
Data format innovations have been critical for machine learning (ML) scaling, which in turn fuels ground-breaking ML capabilities. However, even in the presence of low-precision fo…
Inclusive-PIM: Hardware-Software Co-design for Broad Acceleration on Commercial PIM Architectures
Johnathan Alsop, Shaizeen Aga, Mohamed Ibrahim +3
Continual demand for memory bandwidth has made it worthwhile for memory vendors to reassess processing in memory (PIM), which enables higher bandwidth by placing compute units in/n…
Computation vs. Communication Scaling for Future Transformers on Future Hardware
Suchita Pati, Shaizeen Aga, Mahzabeen Islam +2
Scaling neural network models has delivered dramatic quality gains across ML problems. However, this scaling has increased the reliance on efficient distributed training techniques…