Category Archives: Uncategorized

Accessing LAPACK from the MKL library

Many user programs reference the LAPACK (Linear Algebra PACKage) library. The ARC software library does not include a standalone compiled version of this library. However, a highly tuned version of the LAPACK library is included in the ATLAS library (along with the BLAS, FFT functions, and many others). The optimized versions of LAPACK functions available through the MKL library typically offer better performance than a user could get from a source code library of LAPACK. For details, refer to https://secure.hosting.vt.edu/www.arc.vt.edu/userguide/lapack_mkl/ .

Accessing LAPACK from the ATLAS library

Many user programs reference the LAPACK (Linear Algebra PACKage) library. The ARC software library does not include a standalone compiled version of this library. However, some of the most useful LAPACK functions are available in the ATLAS library, which contains automatically tuned versions of those functions, as well as every BLAS function. Automatic tuning typically offers better performance than a user could get from a source code library of LAPACK. For details, refer to https://secure.hosting.vt.edu/www.arc.vt.edu/userguide/lapack_atlas/ .

Accessing BLAS functions in the MKL Library

Many user programs reference the BLAS (Basic Linear Algebra Subprograms) library. The ARC software library does not include a standalone compiled version of this library. However, the BLAS are available in the Intel MKL library, which contains automatically tuned versions of every BLAS function, offering better performance than a user could get from a source code library of BLAS. For details, refer to https://secure.hosting.vt.edu/www.arc.vt.edu/userguide/blas_mkl/ .

Accessing BLAS functions by using ATLAS

Many user programs reference the BLAS (Basic Linear Algebra Subprograms) library. The ARC software library does not include a standalone compiled version of this library. However, the BLAS are available in the ATLAS library, which contains automatically tuned versions of every BLAS function, offering better performance than a user could get from a source code library of BLAS. For details, refer to https://secure.hosting.vt.edu/www.arc.vt.edu/userguide/blas_atlas/ .

ARC System Access Requires Two Factor Authorization

All users need two factor authorization (2FA) to access VT ARC systems.

Details about setting up and using 2FA are in:

https://secure.hosting.vt.edu/www.arc.vt.edu/2fa/

If you are using the DUO app on a cell phone for your 2FA, then you
can click on the key next to “VT”, and a code will pop up. Then, when
you log into any 2FA system, you can follow your password by a comma and
then the code:

      password,code
    

This alternative is useful for situations where cell phone service is
an issue, or for dealing with systems with a fast timeout, such as ETX.

ARC TA – Hailey Larose/David Haak

Welcome message

Welcome!

Often times in bioinformatics, we need to utilize software and run programs that our normal computers or laptops are unable to handle. When this situation arises, the ARC super-computing environment helps alleviate some of this burden.

This FAQ will cover the programs related to bioinformatics that have been pre-installed and are ready for use on the ARC servers. Continue reading ARC TA – Hailey Larose/David Haak

New ARC Cluster: Cascades

ARC is happy to announce the release of a new cluster, named Cascades, available at cascades1.arc.vt.edu and cascades2.arc.vt.edu. Cascades is a 196-node system capable of tackling the full spectrum of computational workloads, from problems requiring hundreds of compute cores to data-intensive problems requiring large amount of memory and storage resources. Cascade contains three compute engines designed for distinct workloads:

  • General – Distributed, scalable workloads. With Intel’s latest-generation Broadwell processors, 2 16-core processors and 128 GB of memory on each node, this 190-node compute engine is suitable for traditional HPC jobs and large codes using MPI.
  • GPU – Data visualization and code acceleration! There are four nodes in this compute engine which have – two Nvidia K80 GPUs, 512 GB of memory, and one 2 TB NVMe PCIe flash card.
  • Very Large Memory – Graph analytics and very large datasets. With 3TB (3072 gigabytes) of memory, four 18-core processors and 6 1.8TB direct attached SAS hard drives, 400 GB SAS SSD drive, and one 2 TB NVMe PCIe flash card , each of these two servers will enable analysis of large highly-connected datasets, in-memory database applications, and speedier solution of other large problems.

Continue reading New ARC Cluster: Cascades