148 citations · 533 across the 14 of their papers we have counts for
14 papers
Chakra: Advancing Performance Benchmarking and Co-design using Standardized Execution Traces
Srinivas Sridharan, Taekyung Heo, Louis Feng +8
Benchmarking and co-design are essential for driving optimizations and innovation around ML models, ML software, and next-generation hardware. Full workload benchmarks, e.g. MLPerf…
ASTRA-sim2.0: Modeling Hierarchical Networks and Disaggregated Systems for Large-model Training at Scale
William Won, Taekyung Heo, Saeed Rashidi +3
As deep learning models and input data are scaling at an unprecedented rate, it is inevitable to move towards distributed training platforms to fit the model and increase training…
Mystique: Enabling Accurate and Scalable Generation of Production AI Benchmarks
Mingyu Liang, Wenyin Fu, Louis Feng +5
Building large AI fleets to support the rapidly growing DL workloads is an active research topic for modern cloud providers. Generating accurate benchmarks plays an essential role…
Impact of RoCE Congestion Control Policies on Distributed Training of DNNs
Tarannum Khan, Saeed Rashidi, Srinivas Sridharan +3
RDMA over Converged Ethernet (RoCE) has gained significant attraction for datacenter networks due to its compatibility with conventional Ethernet-based fabric. However, the RDMA pr…
Themis: A Network Bandwidth-Aware Collective Scheduling Policy for Distributed Training of DL Models
Saeed Rashidi, William Won, Sudarshan Srinivasan +2
Distributed training is a solution to reduce DNN training time by splitting the task across multiple NPUs (e.g., GPU/TPU). However, distributed training adds communication overhead…
Software-Hardware Co-design for Fast and Scalable Training of Deep Learning Recommendation Models
Dheevatsa Mudigere, Yuchen Hao, Jianyu Huang +50
Deep learning recommendation models (DLRMs) are used across many business-critical services at Facebook and are the single largest AI application in terms of infrastructure demand…
Machine Learning (ML) In a 5G Standalone (SA) Self Organizing Network (SON)
Srinivasan Sridharan
Machine learning (ML) is included in Self-organizing Networks (SONs) that are key drivers for enhancing the Operations, Administration, and Maintenance (OAM) activities. It is incl…
Enabling Compute-Communication Overlap in Distributed Deep Learning Training Platforms
Saeed Rashidi, Matthew Denton, Srinivas Sridharan +4
Deep Learning (DL) training platforms are built by interconnecting multiple DL accelerators (e.g., GPU/TPU) via fast, customized interconnects with 100s of gigabytes (GBs) of bandw…
Deep Learning Training in Facebook Data Centers: Design of Scale-up and Scale-out Systems
Maxim Naumov, John Kim, Dheevatsa Mudigere +12
Large-scale training is important to ensure high performance and accuracy of machine-learning models. At Facebook we use many different models, including computer vision, video and…
Automatic Model Parallelism for Deep Neural Networks with Compiler and Hardware Support
Sanket Tavarageri, Srinivas Sridharan, Bharat Kaul
The deep neural networks (DNNs) have been enormously successful in tasks that were hitherto in the human-only realm such as image recognition, and language translation. Owing to th…
Mixed Precision Training of Convolutional Neural Networks using Integer Operations
Dipankar Das, Naveen Mellempudi, Dheevatsa Mudigere +14
The state-of-the-art (SOTA) for mixed precision training is dominated by variants of low precision floating point operations, and in particular, FP16 accumulating into FP32 Micikev…
On Scale-out Deep Learning Training for Cloud and HPC
Srinivas Sridharan, Karthikeyan Vaidyanathan, Dhiraj Kalamkar +8
The exponential growth in use of large deep neural networks has accelerated the need for training these deep neural networks in hours or even minutes. This can only be achieved thr…
Deep Learning at 15PF: Supervised and Semi-Supervised Classification for Scientific Data
Thorsten Kurth, Jian Zhang, Nadathur Satish +12
This paper presents the first, 15-PetaFLOP Deep Learning system for solving scientific pattern classification problems on contemporary HPC architectures. We develop supervised conv…
Distributed Deep Learning Using Synchronous Stochastic Gradient Descent
Dipankar Das, Sasikanth Avancha, Dheevatsa Mudigere +5
We design and implement a distributed multinode synchronous SGD algorithm, without altering hyper parameters, or compressing data, or altering algorithmic behavior. We perform a de…