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Auswahl der wissenschaftlichen Literatur zum Thema „Parallel code optimization“
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Zeitschriftenartikel zum Thema "Parallel code optimization"
Özcan, Ender, und Esin Onbaşioğlu. „Memetic Algorithms for Parallel Code Optimization“. International Journal of Parallel Programming 35, Nr. 1 (02.12.2006): 33–61. http://dx.doi.org/10.1007/s10766-006-0026-x.
Der volle Inhalt der QuelleLuo, Hao, Guoyang Chen, Pengcheng Li, Chen Ding und Xipeng Shen. „Data-centric combinatorial optimization of parallel code“. ACM SIGPLAN Notices 51, Nr. 8 (09.11.2016): 1–2. http://dx.doi.org/10.1145/3016078.2851182.
Der volle Inhalt der QuelleBailey, Duane A., Janice E. Cuny und Bruce B. MacLeod. „Reducing communication overhead: A parallel code optimization“. Journal of Parallel and Distributed Computing 4, Nr. 5 (Oktober 1987): 505–20. http://dx.doi.org/10.1016/0743-7315(87)90021-9.
Der volle Inhalt der QuelleShang, Zhi. „Large-Scale CFD Parallel Computing Dealing with Massive Mesh“. Journal of Engineering 2013 (2013): 1–6. http://dx.doi.org/10.1155/2013/850148.
Der volle Inhalt der QuelleÖzturan, Can, Balaram Sinharoy und Boleslaw K. Szymanski. „Compiler Technology for Parallel Scientific Computation“. Scientific Programming 3, Nr. 3 (1994): 201–25. http://dx.doi.org/10.1155/1994/243495.
Der volle Inhalt der QuelleKiselev, E. A., P. N. Telegin und A. V. Baranov. „Impact of Parallel Code Optimization on Computer Power Consumption“. Lobachevskii Journal of Mathematics 44, Nr. 12 (Dezember 2023): 5306–19. http://dx.doi.org/10.1134/s1995080223120211.
Der volle Inhalt der QuelleSafarik, Jakub, und Vaclav Snasel. „Acceleration of Particle Swarm Optimization with AVX Instructions“. Applied Sciences 13, Nr. 2 (04.01.2023): 734. http://dx.doi.org/10.3390/app13020734.
Der volle Inhalt der QuelleChowdhary, K. R., Rajendra Purohit und Sunil Dutt Purohit. „Source-to-source translation for code-optimization“. Journal of Information and Optimization Sciences 44, Nr. 3 (2023): 407–16. http://dx.doi.org/10.47974/jios-1350.
Der volle Inhalt der QuelleWANG, SHENGYUE, PEN-CHUNG YEW und ANTONIA ZHAI. „CODE TRANSFORMATIONS FOR ENHANCING THE PERFORMANCE OF SPECULATIVELY PARALLEL THREADS“. Journal of Circuits, Systems and Computers 21, Nr. 02 (April 2012): 1240008. http://dx.doi.org/10.1142/s0218126612400087.
Der volle Inhalt der QuelleSiow, C. L., Jaswar und Efi Afrizal. „Computational Fluid Dynamic Using Parallel Loop of Multi-Cores Processor“. Applied Mechanics and Materials 493 (Januar 2014): 80–85. http://dx.doi.org/10.4028/www.scientific.net/amm.493.80.
Der volle Inhalt der QuelleDissertationen zum Thema "Parallel code optimization"
Cordeiro, Silvio Ricardo. „Code profiling and optimization in transactional memory systems“. reponame:Biblioteca Digital de Teses e Dissertações da UFRGS, 2014. http://hdl.handle.net/10183/97866.
Der volle Inhalt der QuelleTransactional Memory has shown itself to be a promising paradigm for the implementation of shared-memory concurrent applications that eschew a lock-based model of data synchronization. Rather than conditioning exclusive access on the value of a lock that is shared across concurrent threads, Transactional Memory attempts to execute critical sections optimistically, rolling back the modifications in the event of a data access conflict. However, while the lock-based approach has acquired a significant body of debugging, profiling and automated optimization tools (as one of the oldest and most researched synchronization techniques), the field of Transactional Memory is still comparably recent, and programmers are usually tasked with an unguided manual tuning of their transactional applications when facing efficiency problems. We propose a system in which code profiling in a simulated hardware implementation of Transactional Memory is used to characterize a transactional application, which forms the basis for the automated tuning of the underlying speculative system for the efficient execution of that particular application. We also propose a profile-guided approach to the scheduling of threads in a software-based implementation of Transactional Memory, using collected data to predict the likelihood of conflicts and determine what thread to schedule based on this prediction. We present the results achieved under both designs.
Hong, Changwan. „Code Optimization on GPUs“. The Ohio State University, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=osu1557123832601533.
Der volle Inhalt der QuelleFaber, Peter. „Code Optimization in the Polyhedron Model - Improving the Efficieny of Parallel Loop Nests“. kostenfrei, 2007. http://www.opus-bayern.de/uni-passau/volltexte/2008/1251/.
Der volle Inhalt der QuelleFassi, Imen. „XFOR (Multifor) : A new programming structure to ease the formulation of efficient loop optimizations“. Thesis, Strasbourg, 2015. http://www.theses.fr/2015STRAD043/document.
Der volle Inhalt der QuelleWe propose a new programming structure named XFOR (Multifor), dedicated to data-reuse aware programming. It allows to handle several for-loops simultaneously and map their respective iteration domains onto each other. Additionally, XFOR eases loop transformations application and composition. Experiments show that XFOR codes provides significant speed-ups when compared to the original code versions, but also to the Pluto optimized versions. We implemented the XFOR structure through the development of three software tools: (1) a source-to-source compiler named IBB for Iterate-But-Better!, which automatically translates any C/C++ code containing XFOR-loops into an equivalent code where XFOR-loops have been translated into for-loops. IBB takes also benefit of optimizations implemented in the polyhedral code generator CLooG which is invoked by IBB to generate for-loops from an OpenScop specification; (2) an XFOR programming environment named XFOR-WIZARD that assists the programmer in re-writing a program with classical for-loops into an equivalent but more efficient program using XFOR-loops; (3) a tool named XFORGEN, which automatically generates XFOR-loops from any OpenScop representation of transformed loop nests automatically generated by an automatic optimizer
Irigoin, François. „Partitionnement des boucles imbriquées : une technique d'optimisation pour les programmes scientifiques“. Paris 6, 1987. http://www.theses.fr/1987PA066437.
Der volle Inhalt der QuelleHe, Guanlin. „Parallel algorithms for clustering large datasets on CPU-GPU heterogeneous architectures“. Electronic Thesis or Diss., université Paris-Saclay, 2022. http://www.theses.fr/2022UPASG062.
Der volle Inhalt der QuelleClustering, which aims at achieving natural groupings of data, is a fundamental and challenging task in machine learning and data mining. Numerous clustering methods have been proposed in the past, among which k-means is one of the most famous and commonly used methods due to its simplicity and efficiency.Spectral clustering is a more recent approach that usually achieves higher clustering quality than k-means. However, classical algorithms of spectral clustering suffer from a lack of scalability due to their high complexities in terms of number of operations and memory space requirements. This scalability challenge can be addressed by applying approximation methods or by employing parallel and distributed computing.The objective of this thesis is to accelerate spectral clustering and make it scalable to large datasets by combining representatives-based approximation with parallel computing on CPU-GPU platforms. Considering different scenarios, we propose several parallel processing chains for large-scale spectral clustering. We design optimized parallel algorithms and implementations for each module of the proposed chains: parallel k-means on CPU and GPU, parallel spectral clustering on GPU using sparse storage format, parallel filtering of data noise on GPU, etc. Our various experiments reach high performance and validate the scalability of each module and the complete chains
Fang, Juing. „Décodage pondère des codes en blocs et quelques sujets sur la complexité du décodage“. Paris, ENST, 1987. http://www.theses.fr/1987ENST0005.
Der volle Inhalt der QuelleTagliavini, Giuseppe <1980>. „Optimization Techniques for Parallel Programming of Embedded Many-Core Computing Platforms“. Doctoral thesis, Alma Mater Studiorum - Università di Bologna, 2017. http://amsdottorato.unibo.it/8068/1/TESI.pdf.
Der volle Inhalt der QuelleDrebes, Andi. „Dynamic optimization of data-flow task-parallel applications for large-scale NUMA systems“. Thesis, Paris 6, 2015. http://www.theses.fr/2015PA066330/document.
Der volle Inhalt der QuelleWithin the last decade, microprocessor development reached a point at which higher clock rates and more complex micro-architectures became less energy-efficient, such that power consumption and energy density were pushed beyond reasonable limits. As a consequence, the industry has shifted to more energy efficient multi-core designs, integrating multiple processing units (cores) on a single chip. The number of cores is expected to grow exponentially and future systems are expected to integrate thousands of processing units. In order to provide sufficient memory bandwidth in these systems, main memory is physically distributed over multiple memory controllers with non-uniform access to memory (NUMA). Past research has identified programming models based on fine-grained, dependent tasks as a key technique to unleash the parallel processing power of massively parallel general-purpose computing architectures. However, the execution of task-paralel programs on architectures with non-uniform memory access and the dynamic optimizations to mitigate NUMA effects have received only little interest. In this thesis, we explore the main factors on performance and data locality of task-parallel programs and propose a set of transparent, portable and fully automatic on-line mapping mechanisms for tasks to cores and data to memory controllers in order to improve data locality and performance. Placement decisions are based on information about point-to-point data dependences, readily available in the run-time systems of modern task-parallel programming frameworks. The experimental evaluation of these techniques is conducted on our implementation in the run-time of the OpenStream language and a set of high-performance scientific benchmarks. Finally, we designed and implemented Aftermath, a tool for performance analysis and debugging of task-parallel applications and run-times
Child, Ryan. „Performance and Power Optimization of Parallel Discrete Event Simulations Using DVFS“. University of Cincinnati / OhioLINK, 2012. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1342730759.
Der volle Inhalt der QuelleBücher zum Thema "Parallel code optimization"
Faber, Peter. Code Optimization in the Polyhedron Model - Improving the Efficiency of Parallel Loop Nests. Lulu Press, Inc., 2009.
Den vollen Inhalt der Quelle findenFaber, Peter. Paperback: Code Optimization in the Polyhedron Model - Improving the Efficiency of Parallel Loop Nests. Lulu Press, Inc., 2009.
Den vollen Inhalt der Quelle findenPerformance Optimization of Numerically Intensive Codes (Software, Environments and Tools). Society for Industrial Mathematics, 2001.
Den vollen Inhalt der Quelle findenBäck, Thomas. Evolutionary Algorithms in Theory and Practice. Oxford University Press, 1996. http://dx.doi.org/10.1093/oso/9780195099713.001.0001.
Der volle Inhalt der QuelleBuchteile zum Thema "Parallel code optimization"
Dekel, Eliezer, Simeon Ntafos und Shie-Tung Peng. „Parallel tree techniques and code optimization“. In VLSI Algorithms and Architectures, 205–16. Berlin, Heidelberg: Springer Berlin Heidelberg, 1986. http://dx.doi.org/10.1007/3-540-16766-8_18.
Der volle Inhalt der QuelleAndersson, Niclas, und Peter Fritzson. „Object Oriented Mathematical Modelling and Compilation to Parallel Code“. In Applied Optimization, 99–182. Boston, MA: Springer US, 1997. http://dx.doi.org/10.1007/978-1-4613-3400-2_5.
Der volle Inhalt der QuelleSarkar, Vivek. „Challenges in Code Optimization of Parallel Programs“. In Lecture Notes in Computer Science, 1. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-00722-4_1.
Der volle Inhalt der QuelleTaylor, Ryan, und Xiaoming Li. „A Code Merging Optimization Technique for GPU“. In Languages and Compilers for Parallel Computing, 218–36. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-36036-7_15.
Der volle Inhalt der QuelleMartinez Caamaño, Juan Manuel, Willy Wolff und Philippe Clauss. „Code Bones: Fast and Flexible Code Generation for Dynamic and Speculative Polyhedral Optimization“. In Euro-Par 2016: Parallel Processing, 225–37. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-43659-3_17.
Der volle Inhalt der QuelleAvis, David, und Gary Roumanis. „A Portable Parallel Implementation of the lrs Vertex Enumeration Code“. In Combinatorial Optimization and Applications, 414–29. Cham: Springer International Publishing, 2013. http://dx.doi.org/10.1007/978-3-319-03780-6_36.
Der volle Inhalt der QuelleWcisło, R., J. Kitowski und J. Mościński. „Parallelization of a code for animation of multi-object system“. In Applied Parallel Computing Industrial Computation and Optimization, 697–709. Berlin, Heidelberg: Springer Berlin Heidelberg, 1996. http://dx.doi.org/10.1007/3-540-62095-8_75.
Der volle Inhalt der QuelleDamani, Sana, und Vivek Sarkar. „Common Subexpression Convergence: A New Code Optimization for SIMT Processors“. In Languages and Compilers for Parallel Computing, 64–73. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-72789-5_5.
Der volle Inhalt der QuelleEpshteyn, Arkady, María Jesús Garzaran, Gerald DeJong, David Padua, Gang Ren, Xiaoming Li, Kamen Yotov und Keshav Pingali. „Analytic Models and Empirical Search: A Hybrid Approach to Code Optimization“. In Languages and Compilers for Parallel Computing, 259–73. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/978-3-540-69330-7_18.
Der volle Inhalt der QuelleTaubert, Oskar, Marie Weiel, Daniel Coquelin, Anis Farshian, Charlotte Debus, Alexander Schug, Achim Streit und Markus Götz. „Massively Parallel Genetic Optimization Through Asynchronous Propagation of Populations“. In Lecture Notes in Computer Science, 106–24. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-32041-5_6.
Der volle Inhalt der QuelleKonferenzberichte zum Thema "Parallel code optimization"
Sarkar, Vivek. „Code optimization of parallel programs“. In the sixth annual IEEE/ACM international symposium. New York, New York, USA: ACM Press, 2008. http://dx.doi.org/10.1145/1356058.1356087.
Der volle Inhalt der QuelleWang, Fang, Shixin Cheng, Wei Xu und Haifeng Wang. „Design and Code Optimization of Parallel Concatenated Gallager Codes“. In 2007 IEEE 18th International Symposium on Personal, Indoor and Mobile Radio Communications. IEEE, 2007. http://dx.doi.org/10.1109/pimrc.2007.4394240.
Der volle Inhalt der QuelleBuck, Ian. „GPU Computing: Programming a Massively Parallel Processor“. In International Symposium on Code Generation and Optimization (CGO'07). IEEE, 2007. http://dx.doi.org/10.1109/cgo.2007.13.
Der volle Inhalt der QuelleSoliman, Karim, Marwa El Shenawy und Ahmed Abou El Farag. „Loop unrolling effect on parallel code optimization“. In ICFNDS'18: International Conference on Future Networks and Distributed Systems. New York, NY, USA: ACM, 2018. http://dx.doi.org/10.1145/3231053.3231060.
Der volle Inhalt der QuelleLuo, Hao, Guoyang Chen, Pengcheng Li, Chen Ding und Xipeng Shen. „Data-centric combinatorial optimization of parallel code“. In PPoPP '16: 21st ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming. New York, NY, USA: ACM, 2016. http://dx.doi.org/10.1145/2851141.2851182.
Der volle Inhalt der QuelleDubey, A., und T. Clune. „Optimization of a parallel pseudospectral MHD code“. In Proceedings. Frontiers '99. Seventh Symposium on the Frontiers of Massively Parallel Computation. IEEE, 1999. http://dx.doi.org/10.1109/fmpc.1999.750602.
Der volle Inhalt der QuelleSuriana, Patricia, Andrew Adams und Shoaib Kamil. „Parallel associative reductions in Halide“. In 2017 IEEE/ACM International Symposium on Code Generation and Optimization (CGO). IEEE, 2017. http://dx.doi.org/10.1109/cgo.2017.7863747.
Der volle Inhalt der QuelleYongpeng Zhang und F. Mueller. „Hidp: A hierarchical data parallel language“. In 2013 IEEE/ACM International Symposium on Code Generation and Optimization (CGO). IEEE, 2013. http://dx.doi.org/10.1109/cgo.2013.6494994.
Der volle Inhalt der QuelleDewey, Kyle, Vineeth Kashyap und Ben Hardekopf. „A parallel abstract interpreter for JavaScript“. In 2015 IEEE/ACM International Symposium on Code Generation and Optimization (CGO). IEEE, 2015. http://dx.doi.org/10.1109/cgo.2015.7054185.
Der volle Inhalt der QuelleYunsup Lee, R. Krashinsky, V. Grover, S. W. Keckler und K. Asanovic. „Convergence and scalarization for data-parallel architectures“. In 2013 IEEE/ACM International Symposium on Code Generation and Optimization (CGO). IEEE, 2013. http://dx.doi.org/10.1109/cgo.2013.6494995.
Der volle Inhalt der QuelleBerichte der Organisationen zum Thema "Parallel code optimization"
Hisley, Dixie M. Enabling Programmer-Controlled Combined Memory Consistency for Parallel Code Optimization. Fort Belvoir, VA: Defense Technical Information Center, Juli 2003. http://dx.doi.org/10.21236/ada416794.
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