3/2/2023 0 Comments Berkeley upc benchmark utsJan Prins, Jun Huan, Bill Pugh, Chau-Wen Tseng, P. UPC Implementation of an Unbalanced Tree Search Benchmark PDF Workshop on Languages and Compilers for Parallel Computing (LCPC). Stephen Olivier, Jun Huan, Jinze Liu, Jan Prins, James Dinan, P Sadayappan and Chau-Wen Tseng. UTS: An Unbalanced Tree Search Benchmark DOI Parallel and Distributed Systems (PMEO-PDS 2007). ![]() Workshop on Performance Modeling, Evaluation, and Optimization of James Dinan, Stephen Olivier, Jan Prins, Gerald Sabin, P Sadayappan and Chau-Wen Tseng. October, 2008.ĭynamic Load Balancing of Unbalanced Computations Using Message Passing DOI Simulation Modelling Practice and Theory (SIMPAT). James Dinan, Stephen Olivier, Gerald Sabin, Jan Prins, P. ![]() Portland, OR, September 2008.Ī Message Passing Benchmark for Unbalanced Applications DOI of 37th International Conference on Parallel Processing (ICPP-08). Scalable Dynamic Load Balancing Using UPC PDF Explicit Tasks in OpenMP 3.0 (Part of the BOTS benchmarks, Version 1.1+).Chapel (Note: This is a reference implementation and has not been tuned for performance/scalability.).Implementations in additional parallel models include: Our distribution of UTS includes implementations for: When a worker exhausts its work it can get more from the server or the shared queue. In these schemes, workers send new tasks (in the case of UTS these are tree nodes) to the server or enqueue them in the queue. Work sharing can be accomplished through the use of work servers or shared work queues. Work sharing uses a centralized mechanism for distributing work. When a ''thief'' finds a ''victim'' with work, it steals a portion of the victim's work and is able to continue processing. in UPC, OpenMP, Shmem, etc) or in a two sided manner (e.g. Depending on the programming model stealing may be done in a one-sided manner (e.g. Work stealing is a distributed load balancing scheme where idle processors poll their peers in the computation for work. Most schemes fall into two categories: work stealing and work sharing. We have investigated a number of load balancing schemes in the context of UTS. Thus, any static work distribution scheme is highly unlikely to yield good performance for UTS. The size and shape of the tree are random and inferring these parameters from any node in the tree would require a shortcut to solving the SHA-1 secure hash algorithm. ![]() The UTS benchmark presents significant load imbalance problems to any parallelization scheme. Thus, generating a node's children requires multiple applications of the SHA1 hash algorithm to generate splittable hashes for each child. The splittable RNG has been constructed using the SHA1 secure hash algorithm. The tree is generated on the fly using a splittable random number generator (RNG) that allows the random stream to be split and processed in parallel while still producing a deterministic tree. The Unbalanced Tree Search (UTS) benchmark is a parallel benchmarking code that reports the performance achieved when performing an exhaustive search on an unbalanced tree.
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