We meet the needs of today's code developers
The Development Environment Group (DEG) endeavors to provide a stable, usable, leading-edge parallel application development environment that significantly increases the productivity of LLNL applications developers. We strive to do this by enabling better scalable performance and enhancing the reliability of LLNL applications.
DEG partners with its application development user community to identify user requirements and evaluate tool effectiveness. Through collaborations with vendors and other third party software developers, DEG ensures a complete environment in the most cost effective way possible and meets the needs of today’s code developers while steering their code development to exploit emerging technologies.
DEG, part of Livermore Computing, is currently involved in the following projects and activities:
- Compilers—Compilers for Fortran 90/95, Fortran 77, ANSI C, and C++; details are available about compilers currently installed on Livermore Computing platforms.
- Debuggers—See Supported Software and Computing Tools for the available debugging tools, their locations, the machines on which they run, and available documentation (if any).
- Languages—The primary standardized languages used for scientific computing are Fortran, C, and C++. The international organization responsible for standardization in the field of information technology is the International Organization for Standardization/International Electrotechnical Commission (ISO/IEC). The US counterpart is the International Committee for Information Technology Standards (INCITS). JTC 1, SC 22 manages programming languages, their environments, and system software interfaces.
- Parallel tools—Tools are provided on most platforms to allow programmers to take advantage of the parallel nature of the machines. MPI is available on all platforms. See Supported Software and Computing Tools for available parallel tools, their locations, the machines on which they run, and available documentation (if any).
- Performance analysis tools—Various performance analysis tools are available that provide information regarding memory use, hardware counter data, system resource use, and communication. Each tool varies both in ease of use and application perturbation. Several tools provide a GUI for visualization and data reporting. Examination of data is typically done in a postmortem manner; however, some tools have run-time reporting capability. See Supported Software and Computing Tools for available performance analysis tools, their locations, the machines on which they run, and available documentation (if any).
- Scalable I/O—Providing high-performance parallel file system and I/O library support for all major platforms at LLNL, working closely with end users for all parallel I/O issues, performing tests using locally developed tools, and collaborating with platform partners, academic researchers, and vendors to address ASC high-performance I/O needs.
|Scott Futral||futral||DEG group leader; general environment support, Run/Proxy, findentry, flint|
|Dong Ahn||dahn||TotalView support and development projects (including scalability project and BGQ port); scalable debugging tools (including STAT), techniques, and infrastructure (including LaunchMON and Fast Global File Status); hardware performance counter (e.g., PAPI); massively parallel loading (SPINDLE); and next generation resource manager.|
|Blaise Barney||blaiseb||HPC training/workshops, MPI, OpenMP, Pthreads, Totalview, ASC Alliances|
|Greg Becker||becker33||Spack, package management for HPC, tools productization|
|Chris Chambreau||chcham||Memory, profiling, and MPI tracing tool support, including mpiP, TAU, Vampir/VampirTrace, and memP|
|Bor Chan (CASC)||chan1||Benchmarking, performance analysis|
|Chris Earl||earl2||C++ and OpenMP standards support, LLVM compilers and tools|
|Todd Gamblin (CASC)||gamblin2||Performance measurement and analysis; distributed clustering; scalable in-situ analysis techniques; load balance for AMR; run-time systems; collaborative development tools (adept.llnl.gov, Confluence, JIRA, Greenhopper, source hosting, code review, build & test, etc.); CMake and other build systems|
|Alfredo Gimenez||gimenez1||Data analysis and engineering R&D, facility/application monitoring, hardware performance counters|
|Elsa Gonsiorowski||gonsie||Application file I/O and parallel file systems support|
|John Gyllenhaal||gyllen||Valgrind support, linker and POE tricks, compilers, Tool Gear, Qt, DPCL, C/C++|
|Ian Karlin||karlin1||Performance analysis and optimization, benchmarking, LULESH point of contact, Shocx LDRD CS lead|
|Greg Lee||lee218||Parallel tool development, Stack Trace Analysis Tool (STAT), Python support, Intel software (compilers, Inspector, VTune Amplifier, and Pin) support, math libraries (MKL, ACML, Petsc, and FFTW) support.|
|Matt LeGendre||legendre1||Performance analysis tool support, tool component support|
|Edgar Leon||leon||MPI libraries, exascale architectures, process/thread affinity|
|Marty McFadden||mcfadden8||Umpire, umap, OMPD, msr-safe, and spindle support|
|Kathryn Mohror (CASC)||mohror1||Scalable fault tolerant computing, performance measurement and analysis, scalable I/O systems|
|Adam Moody||moody20||MPI and communication performance for Linux, MPI compiler scripts, Dotkit|
|Ramesh Pankajakshan||pankajakshan1||Porting and optimization of field simulation codes to general purpose GPUs using Cuda, RAJA, and MPI; performance analysis and benchmarking|
|David Poliakoff||poliakoff1||C++ template metaprogramming, performance tools, parallel programming abstractions (RAJA), application code support|
|Barry Rountree (CASC)||rountree4||Statistical and algorithmic debugging, power-aware supercomputing|
|Danielle Sikich||sikich1||mpiFileUtils, UnifyCR, data management tools, distributed file systems|
|Local Vendor Support|
|Max Katz||katz12||NVIDIA support expert|
|Roy Musselman||musselman4||IBM application analyst, BGQ compiler and MPI support|