Development Environment

Useful introductory information on LC's software environment is presented in the Software and Development Environment section of the Introduction to Livermore Computing Resources, or the Linux Clusters Overview for system-specific information.

Our Development Environment Software consists of compilers and preprocessors, debugging software, memory-related software, profiling tools, tracing tools, and performance analysis tools.

Python Version 2.6 Site Packages

The following site packages for Python version 2.6 (/usr/local/bin/python2.6) are only supported for CHAOS systems. The current version appears in brackets. Each site package name is a link to the package description.

Site Packages

Note: SSA has been deprecated and will not run in the Intel version 16 and above compilers, and results cannot be viewed in the Inspector version 2016 and above. It can still be run in older compiler and Inspector versions.

Modern x86 processors include vector units that can operate on multiple data objects with a single instruction, otherwise known as Single Instruction, Multiple Data (or SIMD) units. These are implemented in the 128-bit Streaming SIMD Extensions (SSE) and starting with Intel's Sandy Bridge architecture, the 256-bit Advanced Vector eXtensions (AVX).

Vampir is a full featured tool suite for analyzing the performance and message passing characteristics of parallel applications. Vampir is based on run-time tracing of program events collected as OTF format files by other tools/libraries, such as VampirTrace, TAU, Score-P, Open|SpeedShop, etc.

TotalView is a sophisticated and powerful tool used for debugging and analyzing both serial and parallel programs. TotalView provides source level debugging for serial, parallel, multi-process, multi-threaded, accelerator/GPU and hybrid applications written in C/C++ and Fortran. Most HPC platforms and systems are supported. Both a graphical user interface and command line interface are provided.

TAU (Tuning and Analysis Utilities) is a comprehensive profiling and tracing toolkit for performance analysis of parallel programs written in Fortran, C, C++, Java, and Python. It is capable of gathering performance information through instrumentation of functions, methods, basic blocks, and statements. All C++ language features are supported including templates and namespaces.