{"id":18198,"date":"2018-06-05T22:48:29","date_gmt":"2018-06-05T19:48:29","guid":{"rendered":"https:\/\/hgpu.org\/?p=18198"},"modified":"2018-06-05T22:48:29","modified_gmt":"2018-06-05T19:48:29","slug":"the-third-international-workshop-on-gpu-computing-and-ai-gca-2018","status":"publish","type":"post","link":"https:\/\/hgpu.org\/?p=18198","title":{"rendered":"The Third International Workshop on GPU Computing and AI (GCA), 2018"},"content":{"rendered":"

\"The<\/a><\/p>\n

====================================================
\nThe Third International Workshop on GPU Computing and AI (GCA)
\nhttp:\/\/is-candar.org\/GCA18\/<\/a><\/p>\n

to be held in conjunction with<\/p>\n

The Sixth International Symposium on Computing and Networking (CANDAR’18),
\nHida Takayama, Japan, November 27-30, 2018<\/p>\n

http:\/\/is-candar.org\/<\/a>
\n====================================================<\/p>\n

[Introduction]
\nBuilt for massive parallelism, General Purpose computing on Graphic
\nProcessing Unit (GPGPU) has superseded high-performance CPU in several
\nimportant tasks, including computer graphics, physics calculations,
\nencryption\/decryption and scientific computations. Also, GPGPUs have
\nbeen considered as a natural alternative to fulfill the computing
\nneeds for Artificial Intelligence and Machine Learning applications.
\nRecent developments have shown intensive research activity in these
\nfields.<\/p>\n

The goal of this workshop is to provide a forum to discuss and
\nevaluate emerging techniques, platforms and applications, capable of
\nharvesting the power of current GPGPUs as well as to share new ideas
\nfrom the research area of Artificial Intelligence. <\/p>\n

The GCA workshop seeks for high-quality papers on various topics,
\nincluding but not limited to: <\/p>\n

* GPU computing
\n – GPU applications
\n – Computer graphics on GPUs
\n – GPU compilation
\n – GPU programming environments
\n – GPU power efficiency
\n – GPU architectures
\n – GPU theoretical computing models
\n – GPU benchmarking\/measurements
\n – GPU embedded systems
\n – Multi-GPU systems
\n – GPU cluster
\n – Heterogeneous GPU platforms
\n – CPU-GPU cooperation
\n – CUDA\/OpenCL\/OpenACC
\n – Deep learning on GPUs<\/p>\n

* Artificial intelligence
\n – Artificial neural networks
\n – Big data analytics
\n – Data mining
\n – Deep learning
\n – Experts systems
\n – Fuzzy logic
\n – Machine learning
\n – Natural language processing
\n – Computer vision
\n – Reinforcement learning<\/p>\n

[Paper format]
\n* Regular paper: 5-7 pages
\n* Poster paper: 3-4 pages<\/p>\n

[Publication]
\nThe conference and workshop proceedings will be published by Conference
\nPublishing Service and submitted to IEEE Xplore and CSDL digital libraries.
\nAlso, they are submitted for indexing through INSPEC, EI (Compendex),
\nThomson ISI, and other indexing services.<\/p>\n

[Special Issue]
\nA Special Issue, on selected papers from CANDAR’18 main conference and
\nWorkshops, is planned. Please see Special Issue for the details.
\nhttp:\/\/is-candar.org\/special_issue<\/a><\/p>\n

[Important dates]
\n* Abstract submission: August 10, 2018
\n* Paper submission: August 15, 2018
\n* Notification of paper acceptance: October 1, 2018
\n* Submission of camera-ready papers: October 18, 2018<\/p>\n

[Organizers]
\n* Workshop co-chairs
\n – Jacir L. Bordim (University of Brasilia)
\n – Yasuaki Ito (Hiroshima University)<\/p>\n

[Program Committee]
\n* Masaki Aono (Toyohashi University of Technology)
\n* Toshio Endo (Tokyo Institute of Technology)
\n* Ken-ichi Fukui (Osaka University)
\n* Jing Gong (KTH Royal Institute of Technology)
\n* Kohei Hatano (Kyushu University)
\n* Hideitsu Hino (The Institute of Statistical Mathematics)
\n* Tsutomu Inamoto (Ehime University)
\n* Fumihiko Ino (Osaka University)
\n* Takashi Ishida (Tokyo Institute of Technology)
\n* Keisuke Iwai (National Defense Academy)
\n* Yoshinobu Kawahara (Osaka University)
\n* Atsushi Koike (National Institute of Technology, Ichinoseki College)
\n* Marcus Vinicius Lamar (Universidade de Bras\u00edlia)
\n* Marcelo Mandelli (Universidade de Bras\u00edlia)
\n* Daichi Mukunoki (RIKEN Advanced Institute for Computational Science)
\n* Akira Naruse (NVIDIA)
\n* Anto Satriyo Nugroho (BPPT)
\n* Satoshi Ohshima (Kyushu University)
\n* Max Plauth (Hasso Plattner Institute, University of Potsdam)
\n* Janche Sang (Cleveland State University)
\n* Hiroyuki Sato (The University of Tokyo)
\n* Bertil Schmidt (University of Mainz)
\n* Yuichiro Shibata (Nagasaki University)
\n* Takashi Shimokawabe (The University of Tokyo)
\n* Koichi Shirahata (FUJITSU LABORATORIES LTD.)
\n* Tomah Sogabe (The University of Electro-Communications)
\n* Daisuke Takahashi (University of Tsukuba)
\n* Tomoaki Tsumura (Nagoya Institute of Technology)
\n* Cristina Vasconcelos (UFF)
\n* Cheng Wang (University of Houston)
\n* Keiji Yamanaka (Universidade Federal de Uberl\u00e2ndia)
\n* Keiji Yanai (The University of Electro-Communications)
\n* TBA<\/p>\n","protected":false},"excerpt":{"rendered":"

==================================================== The Third International Workshop on GPU Computing and AI (GCA) http:\/\/is-candar.org\/GCA18\/ to be held in conjunction with The Sixth International Symposium on Computing and Networking (CANDAR’18), Hida Takayama, Japan, November 27-30, 2018 http:\/\/is-candar.org\/ ==================================================== [Introduction] Built for massive parallelism, General Purpose computing on Graphic Processing Unit (GPGPU) has superseded high-performance CPU in several important […]<\/p>\n","protected":false},"author":351,"featured_media":0,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_jetpack_memberships_contains_paid_content":false,"footnotes":"","jetpack_publicize_message":"","jetpack_publicize_feature_enabled":true,"jetpack_social_post_already_shared":true,"jetpack_social_options":{"image_generator_settings":{"template":"highway","default_image_id":0,"font":"","enabled":false},"version":2}},"categories":[86],"tags":[],"class_list":["post-18198","post","type-post","status-publish","format-standard","hentry","category-events"],"views":2559,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/18198","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/users\/351"}],"replies":[{"embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=18198"}],"version-history":[{"count":3,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/18198\/revisions"}],"predecessor-version":[{"id":18241,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/18198\/revisions\/18241"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=18198"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=18198"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=18198"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}