Difference between revisions of "PARSEC"

Revision as of 13:19, 9 September 2008

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 diverse selection of applications that is representative of next-generation shared-memory programs for chip-multiprocessors. The benchmark suite with all its applications and input sets is available as open source free of charge from the PARSEC website.

This wiki is here for you. Please feel free to use it to publish whatever information you might have about PARSEC. Be bold!

Distribution

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.

Visits of the PARSEC web site from January 2008 to August 2008. The site was visited over 4000 times.

Workloads

PARSEC 1.0 includes 12 workloads from different application domains. A full documentation of the workloads is available. The following table summarizes the parallelization models and platforms supported by the programs.

Blackscholes

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.

Bodytrack

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.

Canneal

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.

Dedup

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.

Facesim

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.

Ferret

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.

Fluidanimate

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.

Freqmine

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 Concordia University. Freqmine was included in the PARSEC benchmark suite because of the increasing use of data mining techniques.

Streamcluster

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.

Swaptions

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.

Vips

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.

X264

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 make them available to other PARSEC users on this web site. Once you have uploaded the file somewhere you should make an entry in the table below and send an email to the parsec-users mailing list to let people know. If you do not have access to a file hosting service you can use one of the free hosting providers or you can send us the patch by email.

New Features and Extensions

This section is for contributions which go beyond a patch by extending PARSEC into a new direction. New input sets, additional build configurations or entirely new workloads would fit into that category.

Tools and Data

This section lists a few tools and data sources which are useful for the work with PARSEC. Anything relevant for the normal use of the suite to its recreation is listed here.

	Wikimedia Commons [1]   Large repository of images and other media files which can be used under very liberal licenses. A good source for input data for the media workloads. Images of the Wikimedia Commons are used as inputs for the vips benchmark.
	VIPS [2]   Not only a benchmark program but also a tool in itself. The benchmark version of vips that is included in the PARSEC distribution expects input images in its own file format. A full installation of vips can be used to convert files to the vips format. The commands to convert a file `input.jpg' to the vips format are as follows: vips im_sRGB2XYZ input.jpg temp1.v vips im_XYZ2Lab temp1.v temp2.v vips im_Lab2LabQ temp2.v input.v rm -f temp1.v temp2.v This assumes that support for the input file format (JPEG in the example) was enabled at build time. PARSEC disables support for additional file formats by default to reduce the number of dependencies on external libraries.
	Elephants Dream [3]   An open source movie created by the Blender Foundation that can be used freely under a Creative Commons license. The raw PNG images of the movie are hosted by Xiph. The images of the movie were used to create the input videos for the x264 benchmark.
	Ffmpeg [4]   This program is a very fast audio and video converter that can be used under the GNU LGPL and GNU GPL licenses. This tool was used to convert the PNG images into the uncompressed videos that are used as input for x264. The `magic command' that accomplished the conversion was: ffmpeg -f image2 -i %03d.png -pix_fmt yuv420p rawvideo.y4m This command assumes the PNG images are in the same directory and have names that are 3-digit integers starting with 000.png.

Random Stuff

Anything that doesn't fit into the previous categories can be added here.

Related Work

Other benchmark suites include:

• SPEC CPU2006
• SPEC OMP2001
• SPLASH-2
• ALPBench
• BioBench
• BioParallel
• MediaBench II
• NU-Minebench 2.0
• PhysicsBench