Collecting Performance Data with PAPI-C

Terpstra, Daniel; Jagode, Heike; You, Haihang; Dongarra, Jack

doi:10.1007/978-3-642-11261-4_11

Cited by 249 publications

(117 citation statements)

References 9 publications

Supporting

Mentioning

113

Contrasting

Unclassified

Order By: Relevance

“…In order to count the number of floating point operations in each generated local assembly implementation, we have used the Performance Application Programming Interface (PAPI) library [Terpstra et al 2010]. PAPI allows us to count the number of floating point operations in the compiled local assembly implementations by using processor hardware performance counters.…”

Section: Methodsmentioning

confidence: 99%

Optimized code generation for finite element local assembly using symbolic manipulation

Russell

2013

ACM Trans. Math. Softw.

View full text Add to dashboard Cite

Automated code generators for finite element local assembly have facilitated exploration of alternative implementation strategies within generated code. However, even for a theoretical performance indicator such as operation count, an optimal strategy for local assembly is unknown. We explore a code generation strategy based on symbolic integration and polynomial common sub-expression elimination (CSE). We present our implementation of a local assembly code generator using these techniques. We systematically evaluate the approach, measuring operation count, execution time and numerical error using a benchmark suite of synthetic variational forms, comparing against the FEniCS Form Compiler (FFC). Our benchmark forms span complexities chosen to expose the performance characteristics of different code generation approaches. We show that it is possible with additional computational cost, to consistently achieve much of, and sometimes substantially exceed, the performance of alternative approaches without compromising precision. Although the approach of using symbolic integration and CSE for optimizing local assembly is not new, we distinguish our work through our strategies for maintaining numerical precision and detecting common sub-expressions. We discuss the benefits of the symbolic approach for inferring numerical relationships, and analyze the relationship to other proposed techniques which also have greater computational complexity than those of FFC.

show abstract

Section: Methodsmentioning

confidence: 99%

Optimized code generation for finite element local assembly using symbolic manipulation

Russell

2013

ACM Trans. Math. Softw.

View full text Add to dashboard Cite

show abstract

“…To choose among the many such options, we examined the events exposed by the PAPI [2] library over all the benchmarks from Figure 1, each running interference-free, to select the events with minimal extrapolation error. We used the cycles/event ratio present in a single interval of 1 ms to predict the cycles for the next 99 ms using the event count for those 99 intervals (1% sampling).…”

Section: Cpu Time As a Proxy For Application Thread Progressmentioning

confidence: 99%

“…Qlib requires measurements of hardware event counters to indicate application progress when running alone as well as concurrently. It uses the PAPI library [2] to provide access to the hardware event counters and read them out periodically. The PAPI library itself relies on the perfCtr module in linux kernel whose lighter-weight access to counters reduces complexity and overheads; moreover, PAPI is generally useful because it improves portability, and swaps in/out counters if the OS schedules in background threads.…”

Section: Qlib and Qtime: Measuring Online Application Execution mentioning

confidence: 99%

Quality Time: A simple online technique for quantifying multicore execution efficiency

Gupta

Sampson

Taylor

2014

2014 IEEE International Symposium on Performance Analysis of Systems and Software (ISPASS)

View full text Add to dashboard Cite

Abstract-In order to increase utilization, multicore processors share memory resources among an increasing number of cores. This sharing leads to memory interference, which in turn leads to a non-uniform degradation in the execution of concurrent applications, even in the presence of fairness mechanisms. Many utilities rely on application CPU Time both for measuring resource usage and inferring application progress. These utilities are therefore directly affected by the distorting effects of multicore interference on the representativeness of CPU Time as a proxy for progress. This makes reasoning about myriad properties from fairness, to QoS, to throughput optimality very difficult in consolidated environments, such as IaaS.We introduce the notion of Quality Time, which provides a measure of application progress analogous to CPU Time's measure of resource usage, and we propose a simple online sampling-based technique to approximate Quality Time with high accuracy. We have implemented three user-space tools called Qtime, Qtop, and Qplacer. Qtime can attach to an application to calculate its Quality Time online, Qtop is a dashboard that monitors the Quality Times of all applications on the system, and Qplacer leverages Quality Time information to find better application placements and improve overall system quality. With Quality Time, we are able to reduce the error in inferring execution efficiency from 150.3% to 25.1% in the worst case and from 30.0% to 7.5% on average. Qplacer can increase average system throughput by 3.2% when compared to static application placement.

show abstract

“…With the advent of component PAPI (PAPI-C) [2], PAPI has been extended to provide a wider variety of performance data from various sources. Recently a number of new components have been added that provide the ability to measure a system's energy and power usage.…”

Section: Introductionmentioning

confidence: 99%

Measuring Energy and Power with PAPI

Weaver

Johnson

Kasichayanula

et al. 2012

2012 41st International Conference on Parallel Processing Workshops

Self Cite

156

View full text Add to dashboard Cite

Energy and power consumption are becoming critical metrics in the design and usage of high performance systems. We have extended the Performance API (PAPI) analysis library to measure and report energy and power values. These values are reported using the existing PAPI API, allowing code previously instrumented for performance counters to also measure power and energy. Higher level tools that build on PAPI will automatically gain support for power and energy readings when used with the newest version of PAPI.We describe in detail the types of energy and power readings available through PAPI. We support external power meters, as well as values provided internally by recent CPUs and GPUs. Measurements are provided directly to the instrumented process, allowing immediate code analysis in real time. We provide examples showing results that can be obtained with our infrastructure.

show abstract

Collecting Performance Data with PAPI-C

Cited by 249 publications

References 9 publications

Optimized code generation for finite element local assembly using symbolic manipulation

Optimized code generation for finite element local assembly using symbolic manipulation

Quality Time: A simple online technique for quantifying multicore execution efficiency

Measuring Energy and Power with PAPI

Contact Info

Product

Resources

About