The Princeton Application Repository for Shared-Memory Computers (PARSEC) is a benchmark suite composed of multithreaded programs. The suite focuses on emerging workloads and was designed to contain a representative and diverse selection of applications which can be used for performance evaluation and research. The benchmark suite with all its applications and input sets is available as open source free of charge from the PARSEC website.
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PARSEC 1.0 was released at the beginning of 2008. During the first 8 months of the year it was downloaded over 600 times. The benchmark suite is being used for research at numerous institutions such as Intel, Princeton University, Cambridge University, Georgia Tech or MIT. PARSEC workloads were instrumental in the development of the Intel Larrabee processor.
The following workloads are part of PARSEC.
This application is an Intel RMS benchmark. It calculates the prices for a portfolio of European options analytically with the Black-Scholes partial differential equation (PDE). There is no closed-form expression for the Black-Scholes equation and as such it must be computed numerically.
This computer vision application is an Intel RMS workload which tracks a human body with multiple cameras through an image sequence. This benchmark was included due to the increasing significance of computer vision algorithms in areas such as video surveillance, character animation and computer interfaces.
This kernel was developed by Princeton University. It uses cache-aware simulated annealing (SA) to minimize the routing cost of a chip design. Canneal uses fine-grained parallelism with a lock-free algorithm and a very aggressive synchronization strategy that is based on data race recovery instead of avoidance.
This kernel was developed by Princeton University. It compresses a data stream with a combination of global and local compression that is called 'deduplication'. The kernel uses a pipelined programming model to mimic real-world implementations. The reason for the inclusion of this kernel is that deduplication has become a mainstream method for new-generation backup storage systems.
This Intel RMS application was originally developed by Stanford University. It computes a visually realistic animation of the modeled face by simulating the underlying physics. The workload was included in the benchmark suite because an increasing number of animations employ physical simulation to create more realistic effects.
This application is based on the Ferret toolkit which is used for content-based similarity search. It was developed by Princeton University. The reason for the inclusion in the benchmark suite is that it represents emerging next-generation search engines for non-text document data types. In the benchmark, we have configured the Ferret toolkit for image similarity search. Ferret is parallelized using the pipeline model.
This Intel RMS application uses an extension of the Smoothed Particle Hydrodynamics (SPH) method to simulate an incompressible fluid for interactive animation purposes. It was included in the PARSEC benchmark suite because of the increasing significance of physics simulations for animations.
This application employs an array-based version of the FP-growth (Frequent Pattern-growth) method for Frequent Itemset Mining (FIMI). It is an Intel RMS benchmark which was originally developed by Concrdia University. Freqmine was included in the PARSEC benchmark suite because of the increasing use of data mining techniques.
This RMS kernel was developed by Princeton University and solves the online clustering problem. Streamcluster was included in the PARSEC benchmark suite because of the importance of data mining algorithms and the prevalence of problems with streaming characteristics.
The application is an Intel RMS workload which uses the Heath-Jarrow-Morton (HJM) framework to price a portfolio of swaptions. Swaptions employs Monte Carlo (MC) simulation to compute the prices.
This application is based on the VASARI Image Processing System (VIPS) which was originally developed through several projects funded by European Union (EU) grants. The benchmark version is derived from a print on demand service that is offered at the National Gallery of London, which is also the current maintainer of the system. The benchmark includes fundamental image operations such as an affine transformation and a convolution.
This application is an H.264/AVC (Advanced Video Coding) video encoder. H.264 describes the lossy compression of a video stream and is also part of ISO/IEC MPEG-4. The flexibility and wide range of application of the H.264 standard and its ubiquity in next-generation video systems are the reasons for the inclusion of x264 in the PARSEC benchmark suite.
Patches and Bugfixes
If you have any patches or bugfixes for PARSEC you can use the wiki server to host them. Please use a descriptive name to avoid name conflicts. Once you have uploaded the file you should make an entry in the table below and send an email to the parsec-users mailing list to let people know.
Other benchmark suites include: