SCAIGATE is an ambitious project to design the first AI-centric science gateway based on field-programmable gate arrays (FPGAs). The goal is to democratize access to FPGAs and AI in scientific computing and related applications. When completed, the project will enable the large-scale deployment and use of machine learning models on AI-centric FPGA platforms, allowing increased performance-efficiency, reduced development effort, and customization at unprecedented scale, all while simplifying ease-of-use in science domains which were previously AI-lagging.
more »
« less
SCAIGATE: Science Gateway for Scientific Computing with Artificial Intelligence and Reconfigurable Architectures
SCAIGATE is an ambitious project to design the first AI-centric science gateway based on field-programmable gate arrays (FPGAs). The goal is to democratize access to FPGAs and AI in scientific computing and related applications. When completed, the project will enable the large-scale deployment and use of machine learning models on AI-centric FPGA platforms, allowing increased performance-efficiency, reduced development effort, and customization at unprecedented scale, all while simplifying ease-of-use in science domains which were previously AI-lagging. SCAIGATE was an incubation project at the Science Gateway Community Institute (SGCI) bootcamp held in Austin, Texas in 2018.
more »
« less
- Award ID(s):
- 1738420
- NSF-PAR ID:
- 10111121
- Date Published:
- Journal Name:
- Gateway Computing Environments Workshop
- ISSN:
- 2325-5854
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
SLATE (Services Layer at the Edge) is a new project that, when complete, will implement “cyberinfrastructure as code” by augmenting the canonical Science DMZ pattern with a generic, programmable, secure and trusted underlayment platform. This platform will host advanced container-centric services needed for higher-level capabilities such as data transfer nodes, software and data caches, workflow services and science gateway components. SLATE will use best-of-breed data center virtualization components, and where available, software defined networking, to enable distributed automation of deployment and service lifecycle management tasks by domain experts. As such it will simplify creation of scalable platforms that connect research teams, institutions and resources to accelerate science while reducing operational costs and development cycle times. Since SLATE will be designed to require only commodity components for its functional layers, its potential for building distributed systems should extend across all data center types and scales, thus enabling creation of ubiquitous, science-driven cyberinfrastructure. By providing automation and programmatic interfaces to distributed HPC backends and other cyberinfrastructure resources, SLATE will amplify the reach of science gateways and therefore the domain communities they support.more » « less
-
A scalable storage system is an integral requirement for supporting large-scale cloud computing jobs. The raw space on storage systems is made usable with the help of a software layer which is typically called a filesystem (e.g., Google's Cloud Filestore). In this paper, we present the design and implementation of an open-source and free cloud-based filesystem named as "Greyfish" that can be installed on the Virtual Machines (VMs) hosted on different cloud computing systems, such as Jetstream and Chameleon. Greyfish helps in: (1) storing files and directories for different user-accounts in a shared space on the cloud, (2) managing file-access permissions, and (3) purging files when needed. It is currently being used in the implementation of the Gateway-In-A-Box (GIB) project. A simplified version of Greyfish, known as Reef, is already in production in the BOINC@TACC project. Science gateway developers will find Greyfish useful for creating local filesystems that can be mounted in containers. By doing so, they can independently do quick installations of self-contained software solutions in development and test environments while mounting the filesystems on large-scale storage platforms in the production environments only.more » « less
-
null (Ed.)AI and deep learning are experiencing explosive growth in almost every domain involving analysis of big data. Deep learning using Deep Neural Networks (DNNs) has shown great promise for such scientific data analysis applications. However, traditional CPU-based sequential computing without special instructions can no longer meet the requirements of mission-critical applications, which are compute-intensive and require low latency and high throughput. Heterogeneous computing (HGC), with CPUs integrated with GPUs, FPGAs, and other science-targeted accelerators, offers unique capabilities to accelerate DNNs. Collaborating researchers at SHREC1at the University of Florida, CERN Openlab, NERSC2at Lawrence Berkeley National Lab, Dell EMC, and Intel are studying the application of heterogeneous computing (HGC) to scientific problems using DNN models. This paper focuses on the use of FPGAs to accelerate the inferencing stage of the HGC workflow. We present case studies and results in inferencing state-of-the-art DNN models for scientific data analysis, using Intel distribution of OpenVINO, running on an Intel Programmable Acceleration Card (PAC) equipped with an Arria 10 GX FPGA. Using the Intel Deep Learning Acceleration (DLA) development suite to optimize existing FPGA primitives and develop new ones, we were able accelerate the scientific DNN models under study with a speedup from 2.46x to 9.59x for a single Arria 10 FPGA against a single core (single thread) of a server-class Skylake CPU.more » « less
-
more » « less
Abstract The relationship between people, place, and data presents challenges and opportunities for science and society. While there has been general enthusiasm for and work toward Findable, Accessible, Interoperable, and Reusable (FAIR) data for open science, only more recently have these data-centric principles been extended into dimensions important to people and place—notably, the CARE Principles for Indigenous Data Governance, which affect collective benefit, authority to control, responsibility, and ethics. The FAIR Island project seeks to translate these ideals into practice, leveraging the institutional infrastructure provided by scientific field stations. Starting with field stations in French Polynesia as key use cases that are exceptionally well connected to international research networks, FAIR Island builds interoperability between different components of critical research infrastructure, helping connect these to societal benefit areas. The goal is not only to increase reuse of scientific data and the awareness of work happening at the field stations but more generally to accelerate place-based research for sustainable development. FAIR Island works reflexively, aiming to scale horizontally through networks of field stations and to serve as a model for other sites of intensive long-term scientific study.
- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Conference Paper:
- The DOI is not currently available.
