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GPGPU 2022 : 14th Workshop on General Purpose Processing Using GPUConference Series : General Purpose Processing on Graphics Processing Units | |||||||||||||||
Link: https://sarchlab.github.io/gpgpu2022/ | |||||||||||||||
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Call For Papers | |||||||||||||||
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[CFP] The 14th Workshop on General Purpose Processing using GPU (GPGPU 2022) - ------------------------------------------------------------------------------------- Call for papers for GPGPU 2022: The 14th Workshop on General Purpose Processing using GPU held in conjunction with PPOPP 2022, April 2 or 3, 2022, Seoul, South Korea [https://sarchlab.github.io/gpgpu2022/](https://sarchlab.github.io/gpgpu2022/) - ------------------------------------------------------------------------------------ IMPORTANT DATES: Paper submission: Feb 08, 2022 Paper notification: March 1, 2022 Final paper: March 15, 2022 - ------------------------------------------------------------------------------------ DESCRIPTION: Massively parallel (GPUs and other data-parallel accelerators) devices are delivering more and more computing powers required by modern society. With the growing popularity of massively parallel devices, users demand better performance, programmability, reliability, and security. The goal of this workshop is to provide a forum to discuss massively parallel applications, environments, platforms, and architectures, as well as infrastructures that facilitate related research. This year, we are no longer limited to GPU applications and architectures. We welcome research related to any highly parallel computing accelerators and devices. Authors are invited to submit original research papers in the general area of massively parallel computing and architectures. Topics include, but are not limited to: - Security for GPU architecture and other accelerators - AR/VR support using GPUs or other accelerators - Heterogeneous systems - Cloud-based GPU computing - Serverless/disaggregated GPU computing - GPU/accelerator virtualization/containerization - GPU applications - GPU performance evaluation/benchmarking - GPU programming languages - Operating system support for GPU execution - GPU compilation techniques - GPU reliability - GPU hardware architecture for graphics and general-purpose applications - Power-constrained GPU techniques - Multi-GPU systems - Network system design for intra- and inter-accelerator communication - Domain-specific accelerators - Research & design tools for GPU development ------------------------------------------------------------------------------------ SUBMISSION GUIDELINES: Full paper submissions must be in PDF format for US letter-size paper. They must not exceed 6 pages (all inclusive) in standard ACM two-column conference format (review mode, with page numbers and both 9 or 10pt can be used). GPGPU also accepts extended abstracts (2 pages including references). Authors can select if they want to reveal their identity in the submission. Templates for ACM format are available for Microsoft Word and LaTeX at: [https://drupal.sigplan.org/authorInformation.htm](https://drupal.sigplan.org/authorInformation.htm) At least one author must present at the workshop conference. Travel func may be applied through SIGPLAN Professional Activities Committee (PAC). Details are available here [https://www.sigplan.org/PAC/](https://www.sigplan.org/PAC/). Submission Site: [https://easychair.org/conferences/?conf=gpgpu2022](https://easychair.org/conferences/?conf=gpgpu2022) ------------------------------------------------------------------------------------ ORGANIZERS: Co-chair: - Yifan Sun (William & Mary) - Daniel Wong (University of California, Riverside) - Hoda NaghibiJouybari (Binghampton University) Web & Publication Chair - Hongyuan Liu (William & Mary) ------------------------------------------------------------------------------------ Program Committee: - Tor M. Aamodt (University of British Columbia) - José L. Abellán (Universidad Católica de Murcia) - Nael Abu-Ghazaleh (University of California, Riverside) - Zhongliang Chen (AMD) - Shi Dong (Cerebras) - Xiang Gong (Qualcomm) - Hyeran Jeon (University of California, Merced) - Adwait Jog (William & Mary) - David Kaeli (Northeastern University) - Onur Kariyan (AMD) - Gunjae Koo (Korea University) - Jiajia Li (William & Mary) - Ashutosh Pattnaik (ARM) - Seunghee Shin (Binghamton University) - Jieming Yin (Lehigh University) - Jishen Zhao (University of California, San Diego) - Huiyang Zhou (North Carolina State University) |
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