This Intel RMS application was originally developed by Stanford University. The reason for the inclusion of this kernel is that deduplication has become a mainstream method for new-generation backup storage systems. The kernel uses a pipelined programming model to mimic real-world implementations. It compresses a data stream with a combination of global and local compression that is called 'deduplication'. This kernel was developed by Princeton University. 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. It uses cache-aware simulated annealing (SA) to minimize the routing cost of a chip design. This benchmark was included due to the increasing significance of computer vision algorithms in areas such as video surveillance, character animation and computer interfaces.
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This computer vision application is an Intel RMS workload which tracks a human body with multiple cameras through an image sequence. There is no closed-form expression for the Black-Scholes equation and as such it must be computed numerically. It calculates the prices for a portfolio of European options analytically with the Black-Scholes partial differential equation (PDE). This application is an Intel RMS benchmark. Overview of Features and Portability of PARSEC 2.0 Workloads Workload The following table summarizes the parallelization models and platforms supported by the programs.
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#PARSEC DEFINED FULL#
A full documentation of the workloads is available. PARSEC 2.0 includes 13 workloads from different application domains. Visits of the PARSEC web site from January 2008 to August 2008.