More Like this

1. Neocortex and Bridges-2: A High Performance AI+HPC Ecosystem for Science, Discovery, and Societal Good

   https://doi.org/10.1007/978-3-030-68035-0_15  

   Buitrago, P. A.; Nystrom, N. A. (February 2021, Communications in computer and information science)

   Nesmachnow, S.; Castro, H.; Tchernykh, A. (Ed.)

   Artificial intelligence (AI) is transforming research through analysis of massive datasets and accelerating simulations by factors of up to a billion. Such acceleration eclipses the speedups that were made possible though improvements in CPU process and design and other kinds of algorithmic advances. It sets the stage for a new era of discovery in which previously intractable challenges will become surmountable, with applications in fields such as discovering the causes of cancer and rare diseases, developing effective, affordable drugs, improving food sustainability, developing detailed understanding of environmental factors to support protection of biodiversity, and developing alternative energy sources as a step toward reversing climate change. To succeed, the research community requires a high-performance computational ecosystem that seamlessly and efficiently brings together scalable AI, general-purpose computing, and large-scale data management. The authors, at the Pittsburgh Supercomputing Center (PSC), launched a second-generation computational ecosystem to enable AI-enabled research, bringing together carefully designed systems and groundbreaking technologies to provide at no cost a uniquely capable platform to the research community. It consists of two major systems: Neocortex and Bridges-2. Neocortex embodies a revolutionary processor architecture to vastly shorten the time required for deep learning training, foster greater integration of artificial deep learning with scientific workflows, and accelerate graph analytics. Bridges-2 integrates additional scalable AI, high-performance computing (HPC), and high-performance parallel file systems for simulation, data pre- and post-processing, visualization, and Big Data as a Service. Neocortex and Bridges-2 are integrated to form a tightly coupled and highly flexible ecosystem for AI- and data-driven research. 

   more » « less
2. Facilitating Data Discovery for Large-scale Science Facilities using Knowledge Networks

   https://doi.org/10.1109/IPDPS49936.2021.00073  

   Qin, Yubo; Rodero, Ivan; Parashar, Manish (May 2021, 2021 IEEE International Parallel and Distributed Processing Symposium (IPDPS))

   null (Ed.)

   Large-scale multiuser scientific facilities, such as geographically distributed observatories, remote instruments, and experimental platforms, represent some of the largest national investments and can enable dramatic advances across many areas of science. Recent examples of such advances include the detection of gravitational waves and the imaging of a black hole’s event horizon. However, as the number of such facilities and their users grow, along with the complexity, diversity, and volumes of their data products, finding and accessing relevant data is becoming increasingly challenging, limiting the potential impact of facilities. These challenges are further amplified as scientists and application workflows increasingly try to integrate facilities’ data from diverse domains. In this paper, we leverage concepts underlying recommender systems, which are extremely effective in e-commerce, to address these data-discovery and data-access challenges for large-scale distributed scientific facilities. We first analyze data from facilities and identify and model user-query patterns in terms of facility location and spatial localities, domain-specific data models, and user associations. We then use this analysis to generate a knowledge graph and develop the collaborative knowledge-aware graph attention network (CKAT) recommendation model, which leverages graph neural networks (GNNs) to explicitly encode the collaborative signals through propagation and combine them with knowledge associations. Moreover, we integrate a knowledge-aware neural attention mechanism to enable the CKAT to pay more attention to key information while reducing irrelevant noise, thereby increasing the accuracy of the recommendations. We apply the proposed model on two real-world facility datasets and empirically demonstrate that the CKAT can effectively facilitate data discovery, significantly outperforming several compelling state-of-the-art baseline models. 

   more » « less
3. Keep Clear of the Edges : An Empirical Study of Artificial Intelligence Workload Performance and Resource Footprint on Edge Devices

   https://doi.org/10.1109/IPCCC55026.2022.9894338  

   Suo, Kun; Nguyen, Tu N.; Shi, Yong; He, Jing Selena; Hung, Chih-Cheng (November 2022, 2022 IEEE International Performance, Computing, and Communications Conference (IPCCC))

   Recently, with the advent of the Internet of everything and 5G network, the amount of data generated by various edge scenarios such as autonomous vehicles, smart industry, 4K/8K, virtual reality (VR), augmented reality (AR), etc., has greatly exploded. All these trends significantly brought real-time, hardware dependence, low power consumption, and security requirements to the facilities, and rapidly popularized edge computing. Meanwhile, artificial intelligence (AI) workloads also changed the computing paradigm from cloud services to mobile applications dramatically. Different from wide deployment and sufficient study of AI in the cloud or mobile platforms, AI workload performance and their resource impact on edges have not been well understood yet. There lacks an in-depth analysis and comparison of their advantages, limitations, performance, and resource consumptions in an edge environment. In this paper, we perform a comprehensive study of representative AI workloads on edge platforms. We first conduct a summary of modern edge hardware and popular AI workloads. Then we quantitatively evaluate three categories (i.e., classification, image-to-image, and segmentation) of the most popular and widely used AI applications in realistic edge environments based on Raspberry Pi, Nvidia TX2, etc. We find that interaction between hardware and neural network models incurs non-negligible impact and overhead on AI workloads at edges. Our experiments show that performance variation and difference in resource footprint limit availability of certain types of workloads and their algorithms for edge platforms, and users need to select appropriate workload, model, and algorithm based on requirements and characteristics of edge environments. 

   more » « less
4. Applications and Techniques for Fast Machine Learning in Science

   https://doi.org/10.3389/fdata.2022.787421  

   Deiana, Allison McCarn; Tran, Nhan; Agar, Joshua; Blott, Michaela; Di Guglielmo, Giuseppe; Duarte, Javier; Harris, Philip; Hauck, Scott; Liu, Mia; Neubauer, Mark S.; et al (April 2022, Frontiers in Big Data)

   In this community review report, we discuss applications and techniques for fast machine learning (ML) in science—the concept of integrating powerful ML methods into the real-time experimental data processing loop to accelerate scientific discovery. The material for the report builds on two workshops held by the Fast ML for Science community and covers three main areas: applications for fast ML across a number of scientific domains; techniques for training and implementing performant and resource-efficient ML algorithms; and computing architectures, platforms, and technologies for deploying these algorithms. We also present overlapping challenges across the multiple scientific domains where common solutions can be found. This community report is intended to give plenty of examples and inspiration for scientific discovery through integrated and accelerated ML solutions. This is followed by a high-level overview and organization of technical advances, including an abundance of pointers to source material, which can enable these breakthroughs. 

   more » « less
5. First Impressions of the Sapphire Rapids Processor with HBM for Scientific Workloads

   https://doi.org/10.1007/s42979-024-02958-3  

   Siegmann, Eva; Harrison, Robert J; Carlson, David; Chheda, Smeet; Curtis, Anthony; Coskun, Firat; Gonzalez, Raul; Wood, Daniel; Simakov, Nikolay A (June 2024, SN Computer Science)

   Abstract The landscape of high performance computing (HPC) has witnessed exponential growth in processor diversity, architectural complexity, and performance scalability. With an ever-increasing demand for faster and more efficient computing solutions to address an array of scientific, engineering, and societal challenges, the selection of processors for specific applications becomes paramount. Achieving optimal performance requires a deep understanding of how diverse processors interact with diverse workloads, making benchmarking a fundamental practice in the field of HPC. Here, we present preliminary results observed over such benchmarks and applications and a comparison of Intel Sapphire Rapids and Skylake-X, AMD Milan, and Fujitsu A64FX processors in terms of runtime performance, memory bandwidth utilization, and energy consumption. The examples focus specifically on the Sapphire Rapids processor with and without high-bandwidth memory (HBM). An additional case study reports the performance gains from using Intel’s Advanced Matrix Extensions (AMX) instructions, and how they along with HBM can be leveraged to accelerate AI workloads. These initial results aim to give a rough comparison of the processors rather than a detailed analysis and should prove timely and relevant for researchers who may be interested in using Sapphire Rapids for their scientific workloads. 

   more » « less