Oort's working title was Kuiper.
With the wide deployment of AI/ML in our daily lives, the need for data privacy is receiving more attention in recent years. Federated Learning (FL) is an emerging sub-field of machine learning that focuses on in-situ processing of data wherever it is generated. … Continue Reading ››
While training and inference of deep learning models have received significant attention in recent years (e.g., Tiresias, AlloX, and Salus from our group), hyperparamter tuning is often overlooked or put together in the same bucket of optimizations as training. Existing hyperparameter tuning solutions, primarily … Continue Reading ››
Earlier this week, I presented a keynote speech on the state of resource management for deep learning at the HotEdgeVideo'2020 workshop, covering our recent works on systems support for AI (Tiresias, AlloX, and Salus) and discussing open challenges in this space.
We are doing a lot of work on systems for AI in recent years and historically have done some work on application-aware networking. This support will help us combine the two directions to provide datacenter network support for AI workloads both at the edge and inside the network.
Many thanks … Continue Reading ››
While GPUs are always in the news when it comes to deep learning clusters (e.g., Salus or Tiresias), we are in the midst of an emergence of many more computing devices (e.g., FPGAs and problem-specific accelerators), including the traditional CPUs. All of them are compute devices, but one cannot expect the … Continue Reading ››
KLA became our neighbor last year. It turns out building tool-chains for the semiconductor industry requires massive amount of data and image processing in near-realtime as well as addressing corresponding resource and cluster management challenges.
We look forward to working together on related, interesting problems down the line. … Continue Reading ››
With the rising popularity of deep learning, the popularity of GPUs has increased in recent years. Modern GPUs are extremely powerful with a lot of resources. A key challenge in this context is making sure that these devices are highly utilized. Although there has been a lot of research on improving GPU efficiency … Continue Reading ››
This project aims to extend and expand our forays into micro- and macro-level GPU resource management for distributed deep learning applications.
Thanks NSF!
With the advancement of AI in recent years, GPUs have emerged as a popular choice for training deep learning (DL) models on large datasets. To deal with ever-growing datasets, it is also common to run distributed deep learning over multiple GPUs in parallel. Achieving cost-effectiveness and high performance in these clusters relies on … Continue Reading ››
Our position paper calling for a respite in the deep learning framework building arms race has been accepted to appear at this year's HotOS workshop. We make a simple observation: too many frameworks are being proposed with little interoperability between them, even though many target the same or similar workloads; this inevitably leads to repetitions … Continue Reading ››