Overview

ACES (Accelerating Computing for Emerging Sciences), an innovative advanced computational prototype to be developed by Texas A&M University, tries to answer a fundamental question: how does one effectively offer a holistic computing platform that can simultaneously meet the needs of a continuum of users in diverse research communities with varying levels of computing adoption? The project will allow researchers to creatively develop new programming models and workflows that utilize these architectures while simultaneously advancing HPC (High Performance Computing) and data science projects. Starting September 1st, 2023, ACES is currently in the "test-bed" phase.

Our Purpose

AI and ML are integrated with traditional simulation and modeling approaches in the pursuit of innovation. Edge-computing and instrumental probes have pushed the need to verify, process, store, analyze, and query vast amounts of unstructured data in real time. The coupling of analytics with closely-situated data on highly-usable web-based technologies connected to a compute backend have led to a paradigm shift in expectations from research computing environments. The ACES innovative composable hardware platform helps accelerate transformative changes in research areas that can leverage novel accelerators for analytics and computing. ACES leverages Liqid’s composable framework via PCIe (Peripheral Component Interconnect express) hybrid Gen4&5 on Intel’s Sapphire Rapid processors to offer a rich accelerator testbed consisting of Intel Ponte Vecchio GPUs (Graphics Processing Units), Intel FPGAs (Field Programmable Gate Arrays), NVIDIA H100 GPUs, NEC Vector Engines, NextSilicon co-processors, Graphcore IPUs (Intelligence Processing Units). The accelerators are coupled with Intel Optane memory and DDN Lustre storage interconnected with Mellanox NDR 400Gbps (gigabit-per-second) InfiniBand to support workflows that benefit from optimized devices. ACES will enable applications and workflows to dynamically integrate the different accelerators, memory, and in-network computing protocols to glean new insights by rapidly processing large volumes of data, and provide researchers with a unique platform to produce complex hybrid programming models that effectively supports calculations that were not feasible before.

PI/Co-PI Team

• Honggao Liu (Principal Investigator, Texas A&M University)
• Lisa Perez (Co-Principal Investigator, Texas A&M University)
• Dhruva Chakravorty (Co-Principal Investigator, Texas A&M University)
• Shaowen Wang (Co-Principal Investigator, University of Illinois Urbana-Champaign )
• Timothy Cockerill (Co-Principal Investigator, Texas Advanced Computing Center)

Senior Investigator Team

• Francis Dang (Senior Investigator, Texas A&M University)
• Costas Georghiades (Senior Investigator, Texas A&M University)
• Edwin Pierson (Senior Investigator, Texas A&M University)
• William Deigaard (Senior Investigator, Texas A&M University)
• Tsung-I Huang (Senior Investigator, Texas A&M University)
• Xinyue Ye (Senior Investigator, Texas A&M University)
• Zhe Zhang (Senior Investigator, Texas A&M University)
• Jian Tao (Senior Investigator, Texas A&M University)

External Advisory Committee

• Gabrielle Allen (University of Wyoming)
• John Goodhue (MGHPCC)
• Richard Gerber (NERSC)
• Victor Hazlewood (University of Tennessee)
• Dan Katz (NCSA)
• Anita Nikolich (UIUC)
• Carol Song (Purdue)
• Dan Stanzione (TACC at UT Austin)