Beta Configuration



The Beta hardware is a subset of the hardware currently used in Flux.


The compute nodes are all interconnected with InfiniBand networking. In addition to the InfiniBand networking, there is a gigabit Ethernet network that also connects all of the nodes. This is used for node management and NFS file system access.


The high-speed scratch file system is based on Lustre v2.5 and is a DDN SFA10000 backed by the hardware described in this table, the same that is used in Flux:

Server Type

Network Connection

Disk Capacity (raw/usable)

Dell R610 40Gbps InfiniBand 520 TB / 379 TB
Dell R610 40Gbps InfiniBand 530 TB / 386 TB
Dell R610 40Gbps InfiniBand 530 TB / 386 TB
Dell R610 40Gbps InfiniBand 520 TB / 379 TB


160 Gbps

2100 TB / 1530 TB


Computing jobs on Beta are managed completely through Slurm.  See the Beta User Guide for directions on how to submit and manage jobs.


There are three layers of software on Beta.

Operating Software

The Beta cluster runs CentOS 7. We update the operating system on Beta as CentOS releases new versions and our library of third-party applications offers support. Due to the need to support several types of drivers (AFS and Lustre file system drivers, InfiniBand network drivers and NVIDIA GPU drivers) and dozens of third party applications, we are cautious in upgrading and can lag CentOS’s releases by months.

Compilers and Parallel and Scientific Libraries

Beta supports the Gnu Compiler Collection, the Intel Compilers, and the PGI Compilers for C and Fortran. The Beta cluster’s parallel library is OpenMPI, and the default versions are 1.10.7 (i686) and 3.1.2 (x86_64), and there are limited earlier versions available.  Beta provides the Intel Math Kernel Library (MKL) set of high-performance mathematical libraries. Other common scientific libraries are compiled from source and include HDF5, NetCDF, FFTW3, Boost, and others.

Please contact us if you have questions about the availability of, or support for, any other compilers or libraries.

Application Software

Beta supports a wide range of application software. We license common engineering simulation software, for example, Ansys, Abaqus, VASP, and we compile other for use on Beta, for example, OpenFOAM and Abinit. We also have software for statistics, mathematics, debugging and profiling, etc. Please contact us if you wish to inquire about the current availability of a particular application.


Beta has eight K20x GPUs on one node for testing GPU workloads under Slurm.

GPU Model NVidia K20X
Number and Type of GPU one Kepler GK110
Peak double precision floating point perf. 1.31 Tflops
Peak single precision floating point perf. 3.95 Tflops
Memory bandwidth (ECC off) 250 GB/sec
Memory size (GDDR5) 6 GB
CUDA cores 2688

If you have questions, please send email to

Getting Access

Beta is intended for small scale testing to convert Torque/PBS scripts to Slurm. No sensitive data of any type should be used on Beta.

To request:

1. Fill out the ARC-TS HPC account request form.

Because this is a test platform, there is no cost for using Beta.

Related Event

January 18 @ 1:00 am - 4:00 pm

Advanced Graphics Optimization For Data Visualization In Unity3D

Modern 3D game engines and computer hardware can render convincing graphics, rivaling that of pre-rendered 3D animation. But video games still require special optimization techniques and tricks. This relates directly…

January 20 @ 10:00 am - 12:00 pm

Introduction to SPSS: Basics of SPSS

Each section will go over one chapter from the materials at Section 1: Basics of SPSS (1/20, 10am – 12pm) Section 2: Variables (1/27, 10am – 12pm) Section 3:…

January 22 @ 3:00 pm - 4:30 pm

GIS Fundamentals – II (Vector and network data models)

This is the second workshop in a series of workshops we are offering this semester on the fundamentals of GIS. Each workshop covers one or two key elements of GIS…

January 26 @ 10:00 am - 12:00 pm

Introduction to Python’s NumPy library

This workshop will introduce you to the NumPy library in Python, which is useful in scientific computing. We will cover NumPy’s n-dimensional array object and associated functions in depth, along…