48 citations · 73 across the 12 of their papers we have counts for
13 papers
Flover: A Temporal Fusion Framework for Efficient Autoregressive Model Parallel Inference
Jinghan Yao, Nawras Alnaasan, Tian Chen +4
Autoregressive models, despite their commendable performance in a myriad of generative tasks, face challenges stemming from their inherently sequential structure. Inference on thes…
MCR-DL: Mix-and-Match Communication Runtime for Deep Learning
Quentin Anthony, Ammar Ahmad Awan, Jeff Rasley +5
In recent years, the training requirements of many state-of-the-art Deep Learning (DL) models have scaled beyond the compute and memory capabilities of a single processor, and nece…
Performance Characterization of using Quantization for DNN Inference on Edge Devices: Extended Version
Hyunho Ahn, Tian Chen, Nawras Alnaasan +5
Quantization is a popular technique used in Deep Neural Networks (DNN) inference to reduce the size of models and improve the overall numerical performance by exploiting native har…
OMB-Py: Python Micro-Benchmarks for Evaluating Performance of MPI Libraries on HPC Systems
Nawras Alnaasan, Arpan Jain, Aamir Shafi +2
Python has become a dominant programming language for emerging areas like Machine Learning (ML), Deep Learning (DL), and Data Science (DS). An attractive feature of Python is that…
Cross-layer Visualization and Profiling of Network and I/O Communication for HPC Clusters
Pouya Kousha, Quentin Anthony, Hari Subramoni +1
Understanding and visualizing the full-stack performance trade-offs and interplay between HPC applications, MPI libraries, the communication fabric, and the file system is a challe…
Efficient MPI-based Communication for GPU-Accelerated Dask Applications
Aamir Shafi, Jahanzeb Maqbool Hashmi, Hari Subramoni +1
Dask is a popular parallel and distributed computing framework, which rivals Apache Spark to enable task-based scalable processing of big data. The Dask Distributed library forms t…