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| Scalasca | |
|---|---|
| Name | Scalasca |
| Developer | Forschungszentrum Jülich, Technische Universität Dresden, GÉANT (consortia) |
| Released | 2000s |
| Programming language | C, C++, Fortran, Python |
| Operating system | Linux, UNIX |
| Genre | Performance profiling, performance analysis |
| License | BSD-style, open source |
Scalasca is a performance analysis toolset for profiling and tracing parallel applications on high-performance computing systems. It automates detection of inefficiencies and scalability problems in applications written for distributed-memory and shared-memory models, providing measurement, trace analysis, and root-cause identification. Scalasca integrates with established ecosystems for scientific computing and was designed to support optimization workflows for large-scale simulations and codes used on supercomputers.
Scalasca provides automatic runtime instrumentation, event collection, and scalable trace summarization to identify performance bottlenecks in parallel programs. It interoperates with message-passing and threading models and produces reports that map hotspots to source locations and communication patterns. The project emphasizes scalability to thousands of ranks and focuses on latency, load imbalance, and synchronization inefficiencies commonly encountered on machines such as those at Oak Ridge National Laboratory, Argonne National Laboratory, Lawrence Livermore National Laboratory, and European centers like Jülich Research Centre and European Centre for Medium-Range Weather Forecasts.
Scalasca originated from collaborative efforts among research groups specializing in performance engineering and parallel runtime analysis during the early 2000s. Key contributors and partner institutions include Forschungszentrum Jülich, Technische Universität Dresden, and collaborative projects funded through European research initiatives and national laboratories. Development aligned with contemporaneous tools such as TAU (Tuning and Analysis Utilities), HPCToolkit, and Score-P, with cross-fertilization via shared measurement libraries and standards. Over successive releases, Scalasca expanded support for hybrid programming models, integration with build and job workflows used at centers like CERN and NERSC, and adaptation to emerging interconnects from vendors such as IBM, Cray (Hewlett Packard Enterprise), and Intel.
Scalasca comprises several cooperating components: instrumentation and measurement, runtime libraries for event handling, trace compressors and summarizers, and analysis modules that perform pattern detection and root-cause localization. Instrumentation supports compiler-based and manual approaches compatible with toolchains from GNU Compiler Collection, Intel Compilers, and LLVM Clang. The measurement backend interfaces with performance infrastructures like PAPI for hardware counter access and leverages libraries such as MPI implementations from Open MPI and MPICH. Trace processing and report generation integrate with visualization and database tools found in environments like ParaView and ELF-based symbol systems, enabling attribution of events to source files and symbols used in projects like LAMMPS, GROMACS, and OpenFOAM.
Scalasca applies a multi-step methodology: automatic instrumentation or manual marking, scalable tracing of events and metrics, postmortem analysis to detect patterns (e.g., long waits, excessive synchronization), and mapping issues to call paths or source annotations. Analysis uses statistical aggregation, causal ordering of events, and pattern libraries derived from studies of parallel applications from centers including Lawrence Berkeley National Laboratory and Los Alamos National Laboratory. The tool distinguishes between communication-related imbalances, compute-bound hotspots, and I/O delays by correlating runtime traces with counters from sources such as Intel VTune data and Linux perf samples. Root-cause localization leverages call-tree comparison, timeline reconciliation, and event clustering techniques inspired by research from University of Tennessee and Technische Universität München.
Scalasca targets high-performance UNIX-like platforms and is most commonly deployed on Linux-based clusters and supercomputers from vendors such as Cray (Hewlett Packard Enterprise), HPE, IBM, and custom-built HPC installations at centers like Oak Ridge National Laboratory. Language support centers on compiled languages used in scientific computing, notably Fortran, C, and C++, with binding and annotation support for Python-orchestrated workflows. It interoperates with parallel programming interfaces including MPI, OpenMP, and tasking models present in ecosystem projects like OpenACC and Kokkos through indirect instrumentation or user annotations.
Typical usage integrates Scalasca into application build and run phases: compile-time instrumentation or linking with measurement libraries, runtime invocation under job schedulers such as SLURM or PBS Professional, and post-execution analysis on login or analysis nodes. Integration points include performance workflows that combine Scalasca with monitoring stacks like Prometheus (for system metrics), visualization suites such as ParaView and VampirTrace-compatible viewers, and continuous performance regression systems used by projects at European Centre for Medium-Range Weather Forecasts and national labs. Users combine Scalasca outputs with profiling from gprof-derived tools and sampling from systems like perf to triangulate optimization opportunities in applications like NWChem and OpenMPI-using codes.
Scalasca has been cited in performance engineering studies across computational science, particularly in fields that require extreme-scale simulations such as climate modeling, materials science, and computational fluid dynamics. The toolset influenced standards and collaborative projects in performance analysis, including shared efforts reflected in Score-P and other measurement infrastructures. Users at national laboratories and research centers have reported improvements in scalability and reduced time-to-solution for codes involved in campaigns at Argonne National Laboratory, Oak Ridge National Laboratory, and European HPC centers. Academic and industrial adopters reference Scalasca alongside contemporaries like TAU (Tuning and Analysis Utilities), HPCToolkit, and Vampir when documenting performance optimization outcomes.
Category:Performance analysis tools