ACES (Accelerating Computing for Emerging Sciences) is a Texas A&M HPRC testbed built around composable infrastructure: ACES pools GPUs, FPGAs, and other accelerators separately from its CPU nodes and lets you assemble custom node configurations on demand over a high-speed PCIe fabric rather than fixing specific accelerators to specific nodes. This makes it a strong fit for researchers who want to experiment with accelerator combinations rather than commit to a single fixed hardware setup up front.
ACES is also notable for hosting accelerator types that are hard to find elsewhere — including Graphcore IPUs, NextSilicon co-processors, and NEC Vector Engines, alongside more familiar NVIDIA H100 and Intel GPUs/FPGAs. This breadth makes it especially useful for AI/ML researchers and others looking to benchmark or prototype on emerging hardware architectures before committing to a particular accelerator for production-scale work.
Jobs
Queue specifications
Metrics updated 2026-06-16
| Name | Purpose | CPUs | GPUs | RAM | Jobs
30 days
|
Wait Time
30-day trend
|
Wall Time
30-day trend
|
|---|---|---|---|---|---|---|---|
| cpu | General CPU-only jobs | Intel Sapphire Rapid: up to 96 cores | N/A | ~488 GB | 91,196 |
|
|
| gpu | NVIDIA GPU workloads (AI/ML, CUDA, parallel GPU jobs). High-memory GPU nodes | Intel Sapphire Rapid: 96 cores | NVIDIA H100 | ~256–512+ GB, varies | 2,621 |
|
|
| gpu_debug | Short GPU testing/debugging (1 node max) | Intel Sapphire Rapid: 96 cores | NVIDIA A30 | ~488 GB | 628 |
|
|
| pvc | Intel GPU Max (PVC) job | Intel Sapphire Rapid: up to 3,072 cores across 32 nodes | Up to 32 Intel PVC GPUs | ~488 GB | 418 |
|
|
| bittware | FPGA-based workloads and hardware acceleration (2 FPGA devices) | Intel Sapphire Rapid: up to 96 cores across 2 nodes | N/A | ~488 GB | — | — | — |
| nextsilicon | Experimental NextSilicon accelerator workloads (restricted access) | Intel Sapphire Rapid: 96 cores (1 node) | N/A (NextSilicon coprocessor) | ~488 GB | — | — | — |
| nec | Special GPU-based architecture for vectorized HPC applications and MPI-based scientific computing | NEC Vector Engine, Type 20B-P | ~760 GB | — | — | — | |
| gh01 (Grace-Hopper) | High-bandwidth AI/ML and HPC workloads requiring fast CPU-GPU data movement and memory interconnect | NVIDIA GH200 Superchip | NVIDIA H100 | ~256–512+ GB, varies | — | — | — |
Datasets
| Name | Description |
|---|---|
| pytorch-computer-vision-datasets | A collection of standard computer vision datasets formatted for PyTorch, supporting tasks like image classification and object detection. On ACES, these are used to benchmark GPU performance and test distributed deep learning workflows across accelerators. |
| pytorch-language-modelling-datasets | Text-based datasets for training NLP and language models in PyTorch. In ACES, they support benchmarking of large-scale, memory-intensive workloads and evaluating performance of transformer-based models across hardware. |
| tensorflow-computer-vision-datasets | Computer vision datasets optimized for TensorFlow, covering tasks such as classification and segmentation. Within ACES, they enable framework comparisons and validation of TensorFlow pipelines on heterogeneous accelerators. |
| tensorflow-language-modelling-datasets | NLP datasets prepared for TensorFlow, used for language modeling, translation, and text analysis. On ACES, they help evaluate distributed training performance and accelerator efficiency for sequential data workloads. |
| videollama_dataset | A multimodal dataset combining video and text for tasks like video understanding and captioning. In ACES, it is used to test high-throughput, multi-accelerator workflows and benchmark complex AI pipelines. |
Storage
File System
| Directory | Path | Quota | Purge | Backup | Notes |
|---|---|---|---|---|---|
| $HOME | /home/username | 10 GB ~10,000 files | 6 months after account deactivation | Daily | Small scripts, config files, not for general use |
| $SCRATCH | /scratch/user/username | 1TB ~250,000 files | 6 months after account deactivation or when quotas are exceeded. | None | Primary working directory for jobs, not for long-term storage. |
| $PROJECT | /scratch/group/projectid | 5TB ~500,000 files | 90 days after allocation expiration | None | Shared storage for group members |
External Storage
Extra storage is available through Texas A&M HPRC: Google Drive (25GB free, with a paid expansion available), Microsoft OneDrive (25GB free), and HPRC Long Term Storage (paid dedicated storage for longer-term needs). For full details and current rates, see Texas A&M HPRC's Extra Storage Options guide: https://hprc.tamu.edu/kb/Helpful-Pages/Storage/
For data storage policies, please visit https://hprc.tamu.edu/kb/User-Guides/ACES/Policies/#data-storage
File Transfer
ACES supports several file transfer methods depending on your needs — Globus Connect is recommended for most transfers, but alternatives are available for more specific use cases. For details and setup instructions, see Texas A&M HPRC's File Transfer guide: https://hprc.tamu.edu/kb/Helpful-Pages/File-Transfer/
Note: FTP is not recommended, as it does not encrypt usernames, passwords, or data during transfer. Use SFTP whenever possible.
| Supported Methods | Data Transfer Node | URL |
|---|---|---|
| GLOBUS | RECOMMENDED | ACCESS TAMU ACES DTN | https://app.globus.org/dashboard |
| SCP/SFTP | ACCESS TAMU ACES DTN | |
| FTP | ACCESS TAMU ACES DTN | |
| RSYNC | ACCESS TAMU ACES DTN | |
| RCLONE | ACCESS TAMU ACES DTN | |
| GDOWN | ACCESS TAMU ACES DTN | |
| PORTAL | ACCESS TAMU ACES DTN | https://portal.hprc.tamu.edu |
Login to ACES
The recommended method is through the ACES OnDemand portal (https://portal-aces.hprc.tamu.edu) which which provides browser-based access to files, terminals, and interactive applications.
For command-line access, users can connect via SSH using a secure jump-host configuration that routes through aces-jump.hprc.tamu.edu to the login node at login.aces.hprc.tamu.edu. Users must go through the ACES portal and download the pubkey pairs and edit the .ssh/config file. More detailed instructions can be found at https://hprc.tamu.edu/kb/User-Guides/ACES/#ssh-login.
Detailed instructions for SSH setup, including key configuration and connection commands, are available in the ACES documentation and linked setup guides.