Topic: HPC Systems and Software

LLNL’s Stack Trace Analysis Tool helps users quickly identify errors in code running on today’s largest machines.

ROSE, an open-source project maintained by Livermore researchers, provides easy access to complex, automated compiler technology and assistance.

New platforms are improving big data computing on Livermore’s high performance computers.

LLNL researchers are finding some factors are more important in determining HPC application performance than traditionally thought.

Livermore computer scientists have helped create a flexible framework that aids programmers in creating source code that can be used effectively on multiple hardware architectures.

LLNL computer scientists use machine learning to model and characterize the performance and ultimately accelerate the development of adaptive applications.

Livermore Computing staff is enhancing the high-speed InfiniBand data network used in many of its high performance computing and file systems.

Computer scientists are incorporating ZFS into their high performance parallel file systems for better performance and scalability.

Performance analysis of parallel scientific codes is becoming increasingly difficult, and existing tools fall short in revealing the root causes of performance problems. We have developed the HAC model, which allows us to directly compare the data across domains and use data visualization and analysis tools available in other domains.

Fast Global File Status (FGFS) is an open-source package that provides scalable mechanisms and programming interfaces to retrieve global information of a file.

MPI_T is an interface for tools introduced in the 3.0 version of MPI. The interface provides mechanisms for tools to access and set performance and control variables that are exposed by an MPI implementation.

Cram lets you easily run many small MPI jobs within a single, large MPI job by splitting MPI_COMM_WORLD up into many small communicators to run each job in the cram file independently.

libMSR provides a convenient interface to access Model Specific Registers and to allow tools to utilize their full functionality.

A comprehensive understanding of the performance behavior of large-scale simulations requires the ability to compile, analyze, and compare measurements and contexts from many independent sources. Caliper, a general-purpose application introspection system, makes that task easier by connecting various independent context annotations, measurement services, and data processing services.

Spindle improves the library-loading performance of dynamically linked HPC applications. It plugs into the system’s dynamic linker and intercepts its file operations so that only one process (or other small amount) will perform the file operations necessary and share the results with other processes in the job.

PⁿMPI is a thin, low-overhead wrapper library that is automatically generated from mpi.h file and that can be linked by default.

Veritas provides a method for validating proxy applications to ensure that they capture the intended characteristics of their parents.

AutomaDeD is a tool that automatically diagnoses performance and correctness faults in MPI applications. It has two major functionalities: identifying abnormal MPI tasks and code regions and finding the least-progressed task. The tool produces a ranking of MPI processes by their abnormality degree and specifies the regions of code where faults are first manifested.

Application-level resilience is emerging as an alternative to traditional fault tolerance approaches because it provides fault tolerance at a lower cost than traditional approaches.

Working on world-class supercomputers at a U.S. national laboratory was not what Edgar Leon, a native of Mexico, envisioned when he began preparing for university.

To overcome the shortcomings of the analytical and architectural approaches to performance modeling and evaluation, we are developing techniques that emulate the behavior of anticipated future architectures on current machines.

With SCR, jobs run more efficiently, recover more work upon failure, and reduce load on critical shared resources.

Greg Lee helps develop tools designed to boost performance and productivity of Livermore scientists.

Olga Pearce studies how to detect and correct load imbalance in high performance computing applications.

Todd Gamblin leads the PAVE project, which develops performance data visualization techniques that are more intuitive for application scientists.