LIKWID: A Lightweight Performance-Oriented Tool Suite for x86 Multicore Environments

Treibig, Jan; Hager, Georg; Wellein, Gerhard

doi:10.1109/icppw.2010.38

Cited by 438 publications

(249 citation statements)

References 6 publications

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“…For this purpose, likwid-perfctr already provides three performance groups called L2, L3 and MEM [17]. A separate performance group was created to measure the traffic traversing the HA.…”

Section: Validation Of Performance Patternsmentioning

confidence: 99%

“…The LIKWID tool suite [17] includes the likwid-bench microbenchmarking framework, which provides a set of assembly language kernels. They cover a variety of streaming access schemes.…”

Section: Validation Of Performance Patternsmentioning

confidence: 99%

“…Since that time, HPM gained more and more attention in the computer science community and consequently a lot of HPM related tools were developed. Some provide basic access to the HPM registers with some additional features like LIKWID [17], PAPI [12] or the kernel interface perf_event [4]. Furthermore, some higher level analysis tools gather additional information by combining the HPM counts with application level traces.…”

Section: Introduction and Related Workmentioning

confidence: 99%

See 2 more Smart Citations

Validation of Hardware Events for Successful Performance Pattern Identification in High Performance Computing

Röhl

Eitzinger

Hager

et al. 2016

Tools for High Performance Computing 2015

Self Cite

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Hardware performance monitoring (HPM) is a crucial ingredient of performance analysis tools. While there are interfaces like LIKWID, PAPI or the kernel interface perf_event which provide HPM access with some additional features, many higher level tools combine event counts with results retrieved from other sources like function call traces to derive (semi-)automatic performance advice. However, although HPM is available for x86 systems since the early 90s, only a small subset of the HPM features is used in practice. Performance patterns provide a more comprehensive approach, enabling the identification of various performancelimiting effects. Patterns address issues like bandwidth saturation, load imbalance, non-local data access in ccNUMA systems, or false sharing of cache lines. This work defines HPM event sets that are best suited to identify a selection of performance patterns on the Intel Haswell processor. We validate the chosen event sets for accuracy in order to arrive at a reliable pattern detection mechanism and point out shortcomings that cannot be easily circumvented due to bugs or limitations in the hardware.

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“…For this purpose, likwid-perfctr already provides three performance groups called L2, L3 and MEM [17]. A separate performance group was created to measure the traffic traversing the HA.…”

Section: Validation Of Performance Patternsmentioning

confidence: 99%

“…The LIKWID tool suite [17] includes the likwid-bench microbenchmarking framework, which provides a set of assembly language kernels. They cover a variety of streaming access schemes.…”

Section: Validation Of Performance Patternsmentioning

confidence: 99%

Section: Introduction and Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Validation of Hardware Events for Successful Performance Pattern Identification in High Performance Computing

Röhl

Eitzinger

Hager

et al. 2016

Tools for High Performance Computing 2015

Self Cite

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show abstract

“…If we also know the node's hardware topology, we can associate a memory sample with a specific portion of it. Tools such as hwloc [5] and likwid [6] exist to detect the available hardware topology on a system. We use a modification of likwid suited to integrate with our existing codebase.…”

Section: A Node Hardware Topologymentioning

confidence: 99%

Dissecting On-Node Memory Access Performance: A Semantic Approach

Giménez

Gamblin

Rountree

et al. 2014

SC14: International Conference for High Performance Computing, Networking, Storage and Analysis

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Abstract-Optimizing memory access is critical for performance and power efficiency. CPU manufacturers have developed sampling-based performance measurement units (PMUs) that report precise costs of memory accesses at specific addresses. However, this data is too low-level to be meaningfully interpreted and contains an excessive amount of irrelevant or uninteresting information.We have developed a method to gather fine-grained memory access performance data for specific data objects and regions of code with low overhead and attribute semantic information to the sampled memory accesses. This information provides the context necessary to more effectively interpret the data. We have developed a tool that performs this sampling and attribution and used the tool to discover and diagnose performance problems in real-world applications. Our techniques provide useful insight into the memory behavior of applications and allow programmers to understand the performance ramifications of key design decisions: domain decomposition, multi-threading, and data motion within distributed memory systems.

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“…The likwid lightweight performance tools project [30] allows accessing performance counters by bypassing the Linux kernel and directly accessing hardware. This can have low overhead but can conflict with concurrent use of other tools accessing the counters.…”

Section: Related Workmentioning

confidence: 99%

Power monitoring with PAPI for extreme scale architectures and dataflow-based programming models

McCraw

Ralph

Danalis

et al. 2014

2014 IEEE International Conference on Cluster Computing (CLUSTER)

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Abstract-For more than a decade, the PAPI performancemonitoring library has provided a clear, portable interface to the hardware performance counters available on all modern CPUs and other components of interest (e.g., GPUs, network, and I/O systems). Most major end-user tools that application developers use to analyze the performance of their applications rely on PAPI to gain access to these performance counters.One of the critical roadblocks on the way to larger, more complex high performance systems, has been widely identified as being the energy efficiency constraints. With modern extreme scale machines having hundreds of thousands of cores, the ability to reduce power consumption for each CPU at the software level becomes critically important, both for economic and environmental reasons. In order for PAPI to continue playing its well established role in HPC, it is pressing to provide valuable performance data that not only originates from within the processing cores but also delivers insight into the power consumption of the system as a whole.An extensive effort has been made to extend the Performance API to support power monitoring capabilities for various platforms. This paper provides detailed information about three components that allow power monitoring on the Intel Xeon Phi and Blue Gene/Q. Furthermore, we discuss the integration of PAPI in PARSEC -a task-based dataflow-driven execution engine -enabling hardware performance counter and power monitoring at true task granularity.

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LIKWID: A Lightweight Performance-Oriented Tool Suite for x86 Multicore Environments

Cited by 438 publications

References 6 publications

Validation of Hardware Events for Successful Performance Pattern Identification in High Performance Computing

Validation of Hardware Events for Successful Performance Pattern Identification in High Performance Computing

Dissecting On-Node Memory Access Performance: A Semantic Approach

Power monitoring with PAPI for extreme scale architectures and dataflow-based programming models

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