Verfügbarkeit: Massively parallel evolutionary computation on GPGPUs

Massively parallel evolutionary computation on GPGPUs:

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Bibliographische Detailangaben
Format:	Elektronisch E-Book
Sprache:	English
Veröffentlicht:	Berlin [u.a.] Springer 2013
Schriftenreihe:	Natural computing series
Schlagworte:	Grafikprozessor Optimierungsproblem Wissenschaftliches Rechnen Massive Parallelität Evolutionärer Algorithmus Hochleistungsrechnen Aufsatzsammlung
Online-Zugang:	BTU01 FHA01 FHM01 FHN01 FKE01 UBG01 UBR01 UBT01 UBW01 UER01 UPA01 Volltext Inhaltsverzeichnis Abstract
Beschreibung:	1 Online-Ressource
ISBN:	9783642379581 9783642379598
DOI:	10.1007/978-3-642-37959-8

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adam_text	MASSIVELY PARALLEL EVOLUTIONARY COMPUTATION ON GPGPUS / : 2013 TABLE OF CONTENTS / INHALTSVERZEICHNIS CHAP. 1 WHY GPGPUS FOR EVOLUTIONARY COMPUTATION? CHAP. 2 UNDERSTANDING NVIDIA GPGPU HARDWARE CHAP. 3 AUTOMATIC PARALLELIZATION OF EC ON GPGPUS AND CLUSTERS OF GPGPU MACHINES WITH EASEA AND EASEA-CLOUD CHAP. 4 GENERIC LOCAL SEARCH (MEMETIC) ALGORITHM ON A SINGLE GPGPU CHIP CHAP. 5 ARGA: ADAPTIVE RESOLUTION MICRO-GENETIC ALGORITHM WITH TABU SEARCH TO SOLVE MINLP PROBLEMS USING GPU CHAP. 6 AN ANALYTICAL STUDY OF GPU COMPUTATION BY PARALLEL GA WITH INDEPENDENT RUNS CHAP. 7 MANY-THREADED DIFFERENTIAL EVOLUTION ON THE GPU CHAP. 8 SCHEDULING USING MULTIPLE SWARM PARTICLE OPTIMIZATION WITH MEMETIC FEATURES ON GRAPHICS PROCESSING UNITS CHAP. 9 ACO WITH TABU SEARCH ON GPUS FOR FAST SOLUTION OF THE QAP CHAP. 10 NEW IDEAS IN PARALLEL METAHEURISTICS ON GPU: SYSTOLIC GENETIC SEARCH CHAP. 11 GENETIC PROGRAMMING ON GPGPU CARDS USING EASEA CHAP. 12 CARTESIAN GENETIC PROGRAMMING ON THE GPU CHAP. 13 IMPLEMENTATION TECHNIQUES FOR MASSIVELY PARALLEL MULTI-OBJECTIVE OPTIMIZATION CHAP. 14 DATA MINING USING PARALLEL MULTI-OBJECTIVE EVOLUTIONARY ALGORITHMS ON GRAPHICS PROCESSING UNITS CHAP. 15 LARGE-SCALE BIOINFORMATICS DATA MINING WITH PARALLEL GENETIC PROGRAMMING ON GRAPHICS PROCESSING UNITS CHAP. 16 GPU-ACCELERATED HIGH-ACCURACY MOLECULAR DOCKING USING GUIDED DIFFERENTIAL EVOLUTION CHAP. 17 USING LARGE-SCALE PARALLEL SYSTEMS FOR COMPLEX CRYSTALLOGRAPHIC PROBLEMS IN MATERIALS SCIENCE CHAP. 18 ARTIFICIAL CHEMISTRIES ON GPU CHAP. 19 ACCELERATION OF GENETIC ALGORITHMS FOR SUDOKU SOLUTION ON MANY-CORE PROCESSORS DIESES SCHRIFTSTUECK WURDE MASCHINELL ERZEUGT. MASSIVELY PARALLEL EVOLUTIONARY COMPUTATION ON GPGPUS / : 2013 ABSTRACT / INHALTSTEXT EVOLUTIONARY ALGORITHMS (EAS) ARE METAHEURISTICS THAT LEARN FROM NATURAL COLLECTIVE BEHAVIOR AND ARE APPLIED TO SOLVE OPTIMIZATION PROBLEMS IN DOMAINS SUCH AS SCHEDULING, ENGINEERING, BIOINFORMATICS, AND FINANCE. SUCH APPLICATIONS DEMAND ACCEPTABLE SOLUTIONS WITH HIGH-SPEED EXECUTION USING FINITE COMPUTATIONAL RESOURCES. THEREFORE, THERE HAVE BEEN MANY ATTEMPTS TO DEVELOP PLATFORMS FOR RUNNING PARALLEL EAS USING MULTICORE MACHINES, MASSIVELY PARALLEL CLUSTER MACHINES, OR GRID COMPUTING ENVIRONMENTS. RECENT ADVANCES IN GENERAL-PURPOSE COMPUTING ON GRAPHICS PROCESSING UNITS (GPGPU) HAVE OPENED UP THIS POSSIBILITY FOR PARALLEL EAS, AND THIS IS THE FIRST BOOK DEDICATED TO THIS EXCITING DEVELOPMENT. THE THREE CHAPTERS OF PART I ARE TUTORIALS, REPRESENTING A COMPREHENSIVE INTRODUCTION TO THE APPROACH, EXPLAINING THE CHARACTERISTICS OF THE HARDWARE USED, AND PRESENTING A REPRESENTATIVE PROJECT TO DEVELOP A PLATFORM FOR AUTOMATIC PARALLELIZATION OF EVOLUTIONARY COMPUTING (EC) ON GPGPUS. THETEN CHAPTERS IN PART II FOCUS ON HOW TO CONSIDER KEY EC APPROACHES IN THE LIGHT OF THIS ADVANCED COMPUTATIONAL TECHNIQUE, IN PARTICULAR ADDRESSING GENERIC LOCAL SEARCH, TABU SEARCH, GENETIC ALGORITHMS, DIFFERENTIAL EVOLUTION, SWARM OPTIMIZATION, ANT COLONY OPTIMIZATION, SYSTOLIC GENETIC SEARCH, GENETIC PROGRAMMING, AND MULTIOBJECTIVE OPTIMIZATION. THESIX CHAPTERS IN PART III PRESENT SUCCESSFUL RESULTS FROM REAL-WORLD PROBLEMS IN DATA MINING, BIOINFORMATICS, DRUG DISCOVERY, CRYSTALLOGRAPHY, ARTIFICIAL CHEMISTRIES, AND SUDOKU. ALTHOUGH THE PARALLELISM OF EAS IS SUITED TO THE SINGLE-INSTRUCTION MULTIPLE-DATA (SIMD)-BASED GPU, THERE ARE MANY ISSUES TO BE RESOLVED IN DESIGN AND IMPLEMENTATION, AND A KEY FEATURE OF THE CONTRIBUTIONS IS THE PRACTICAL ENGINEERING ADVICE OFFERED. THIS BOOK WILL BE OF VALUE TO RESEARCHERS, PRACTITIONERS, AND GRADUATE STUDENTS IN THE AREAS OF EVOLUTIONARY COMPUTATION AND SCIENTIFIC COMPUTING DIESES SCHRIFTSTUECK WURDE MASCHINELL ERZEUGT.
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publishDateSort	2013
publisher	Springer
record_format	marc
series2	Natural computing series
spelling	Massively parallel evolutionary computation on GPGPUs Shigeyoshi Tsutsui ..., ed. Berlin [u.a.] Springer 2013 1 Online-Ressource txt rdacontent c rdamedia cr rdacarrier Natural computing series Grafikprozessor (DE-588)4582114-8 gnd rswk-swf Optimierungsproblem (DE-588)4390818-4 gnd rswk-swf Wissenschaftliches Rechnen (DE-588)4338507-2 gnd rswk-swf Massive Parallelität (DE-588)4324752-0 gnd rswk-swf Evolutionärer Algorithmus (DE-588)4366912-8 gnd rswk-swf Hochleistungsrechnen (DE-588)4532701-4 gnd rswk-swf 1\p (DE-588)4143413-4 Aufsatzsammlung gnd-content Grafikprozessor (DE-588)4582114-8 s Evolutionärer Algorithmus (DE-588)4366912-8 s Massive Parallelität (DE-588)4324752-0 s Optimierungsproblem (DE-588)4390818-4 s Hochleistungsrechnen (DE-588)4532701-4 s Wissenschaftliches Rechnen (DE-588)4338507-2 s 2\p DE-604 Tsutsui, Shigeyoshi 1944- Sonstige (DE-588)1047122847 oth https://doi.org/10.1007/978-3-642-37959-8 Verlag Volltext Springer Fremddatenuebernahme application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=027096197&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis Springer Fremddatenuebernahme application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=027096197&sequence=000003&line_number=0002&func_code=DB_RECORDS&service_type=MEDIA Abstract 1\p cgwrk 20201028 DE-101 https://d-nb.info/provenance/plan#cgwrk 2\p cgwrk 20201028 DE-101 https://d-nb.info/provenance/plan#cgwrk
spellingShingle	Massively parallel evolutionary computation on GPGPUs Grafikprozessor (DE-588)4582114-8 gnd Optimierungsproblem (DE-588)4390818-4 gnd Wissenschaftliches Rechnen (DE-588)4338507-2 gnd Massive Parallelität (DE-588)4324752-0 gnd Evolutionärer Algorithmus (DE-588)4366912-8 gnd Hochleistungsrechnen (DE-588)4532701-4 gnd
subject_GND	(DE-588)4582114-8 (DE-588)4390818-4 (DE-588)4338507-2 (DE-588)4324752-0 (DE-588)4366912-8 (DE-588)4532701-4 (DE-588)4143413-4
title	Massively parallel evolutionary computation on GPGPUs
title_auth	Massively parallel evolutionary computation on GPGPUs
title_exact_search	Massively parallel evolutionary computation on GPGPUs
title_full	Massively parallel evolutionary computation on GPGPUs Shigeyoshi Tsutsui ..., ed.
title_fullStr	Massively parallel evolutionary computation on GPGPUs Shigeyoshi Tsutsui ..., ed.
title_full_unstemmed	Massively parallel evolutionary computation on GPGPUs Shigeyoshi Tsutsui ..., ed.
title_short	Massively parallel evolutionary computation on GPGPUs
title_sort	massively parallel evolutionary computation on gpgpus
topic	Grafikprozessor (DE-588)4582114-8 gnd Optimierungsproblem (DE-588)4390818-4 gnd Wissenschaftliches Rechnen (DE-588)4338507-2 gnd Massive Parallelität (DE-588)4324752-0 gnd Evolutionärer Algorithmus (DE-588)4366912-8 gnd Hochleistungsrechnen (DE-588)4532701-4 gnd
topic_facet	Grafikprozessor Optimierungsproblem Wissenschaftliches Rechnen Massive Parallelität Evolutionärer Algorithmus Hochleistungsrechnen Aufsatzsammlung
url	https://doi.org/10.1007/978-3-642-37959-8 http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=027096197&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=027096197&sequence=000003&line_number=0002&func_code=DB_RECORDS&service_type=MEDIA
work_keys_str_mv	AT tsutsuishigeyoshi massivelyparallelevolutionarycomputationongpgpus

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