]> 2026-09-08T16:38:29+09:00 Lesson:exploiting gpus in virtual machine for biocloud b3cdd293 3000 ko 장시간 GPU batch/HPC job을 호스팅하는 VM 환경에 해당하며, fine-grained multi-tenant inference에는 그대로 일반화할 수 없다. Xen에서 PCIe direct pass-through로 GPU를 VM에 직접 연결하고, PCIe hot plug-in/out과 GPU-Admin/GPU-Manager로 workload 단위 coarse-grained sharing을 수행한다. S3ResearchAgent high Publication scope: international. Lab publication metadata: 1. Heeseung Jo, Jinkyu Jeong, Myoungho Lee, and Dong Hoon Choi, "Exploiting GPUs in Virtual Machine for BioCloud," Journal of Biomedicine and Biotechnology, vol. 2013, article ID 939460, pages 11, Apr. 2013 Verification level: full_text. Sources checked: https://doi.org/10.1155/2013/939460 https://pmc.ncbi.nlm.nih.gov/articles/PMC3654629/ https://pubmed.ncbi.nlm.nih.gov/23710465/ 2026-07-16T15:07:43Z 2461238.1303588 Publication record 1: Heeseung Jo, Jinkyu Jeong, Myoungho Lee, and Dong Hoon Choi, "Exploiting GPUs in Virtual Machine for BioCloud," Journal of Biomedicine and Biotechnology, vol. 2013, article ID 939460, pages 11, Apr. 2013 sharing 시나리오는 synthetic하고 효과는 workload 실행시간·idle time·GPU 수에 따라 달라진다. coarse-grained exclusivity라서 짧고 동시성이 높은 workload보다 장시간 bioinformatics workload에 맞는다. Technical interpretation: device passthrough의 near-native datapath와 hot-plug 기반 coarse-grained multiplexing을 분리하면 장시간 accelerator job에서 isolation·throughput·sharing을 함께 얻을 수 있다. Exploiting GPUs in Virtual Machine for BioCloud BlackScholes의 virtualization overhead는 0.5% 미만(비교 대상은 25–73%), 네 bioinformatics application의 전체 실행 overhead는 평균 3%, 최대 10%였다. hot plug-in/out은 각각 1.3±0.1초였다. 네 VM이 한 GPU를 공유할 때 idle/sleep 구간에 따라 총 실행시간이 20–53% 줄었다. 한 GPU일 때 평균 대기 25.05초, 두 GPU일 때 4.15초였다. bioinformatics처럼 긴 GPU workload에 대해 VM이 GPU를 near-native 성능으로 쓰면서 여러 VM이 GPU를 time-share할 수 있는가? lab device passthrough의 near-native datapath와 hot-plug 기반 coarse-grained multiplexing을 분리하면 장시간 accelerator job에서 isolation·throughput·sharing을 함께 얻을 수 있다. Draft 2026-07-18T14:58:30Z 2461240.1239583 2026-07-18T14:58:30Z 2461240.1239583 exploiting gpus in virtual machine for biocloud b3cdd293 3000 ev_bd717984002c4aeb 2026-07-16T15:07:44Z 2461238.1303704 S3ResearchAgent Heeseung Jo, Jinkyu Jeong, Myoungho Lee, and Dong Hoon Choi, "Exploiting GPUs in Virtual Machine for BioCloud," Journal of Biomedicine and Biotechnology, vol. 2013, article ID 939460, pages 11, Apr. 2013 paper Source: Yonsei University Computer Systems Laboratory publication list supplied by the user. Manifestation 1 of 1. not_recorded exploiting gpus in virtual machine for biocloud b3cdd293# 9e8c603d52f367777e0e125dffb1b21a 3000 canonical-paper-v2-b3cdd293 2026-07-18T05:20:47Z 2461239.7227662 S3ResearchAgent Heeseung Jo, Jinkyu Jeong, Myoungho Lee, and Dong Hoon Choi, "Exploiting GPUs in Virtual Machine for BioCloud" Journal of Biomedicine and Biotechnology, vol. 2013, article ID 939460, pages 11, Apr. 2013 paper 정본 Lesson 보강 근거. 검토 원본: Lesson:technical_review_exploiting_gpus_in_virtual_machine_for_biocloud_9e8875c0. 확인 범위: full_text. 확인한 자료: https://doi.org/10.1155/2013/939460 ; https://pmc.ncbi.nlm.nih.gov/articles/PMC3654629/ ; https://pubmed.ncbi.nlm.nih.gov/23710465/. 질문, 방법, 평가, 해석, 재사용 교훈, 적용 범위와 한계를 같은 Lesson 본문에 통합했습니다. full_text exploiting gpus in virtual machine for biocloud b3cdd293# ff61f908497a9074347ad1f5797e298d 3000 2026-07-18T14:58:30Z 2461240.1239583 S3ResearchAgent verify_50817dfb71c6c9613cb9 metadata_only context 수집 manifest의 실패 원장만 보존되어 원문 주장을 검증하지 못함. inconclusive 689990da723c183d73e5102f6b5a7c8972497dfa210f09dd79bb20472fb857d5 R2-RESTIC:7f893ca5afd2cfb6fe320e9b61063ccc70e75a7a96589420038c8cf338b273be; archive-manifest-sha256=e28171fb69e141ce306d92dfe4b10e6cdc6e81d4fa910c30a846204dbcf8edf8; sha256=689990da723c183d73e5102f6b5a7c8972497dfa210f09dd79bb20472fb857d5 보존 파일 manifest.json ev_bd717984002c4aeb d7721af999253527eb4906d47bd6723d945d03dae141c3b9426cdd711c76be42 exploiting gpus in virtual machine for biocloud b3cdd293# a2a895ac474aa808eb811c72418d454e 3000 2026-07-18T14:58:30Z 2461240.1239583 S3ResearchAgent verify_1f01a2cf93ac6362d6fe full_text observation,interpretation,reusable_lesson observation=supported; interpretation=supported; reusable_lesson=supported supports 9660f6d73e1256bbb59b71743e5b969dbeb1779882375723ea0afc47626ae05b R2-RESTIC:7f893ca5afd2cfb6fe320e9b61063ccc70e75a7a96589420038c8cf338b273be; archive-manifest-sha256=e28171fb69e141ce306d92dfe4b10e6cdc6e81d4fa910c30a846204dbcf8edf8; sha256=9660f6d73e1256bbb59b71743e5b969dbeb1779882375723ea0afc47626ae05b; independently adjudicated claim-bearing primary source Sections 3.1-3.3 and 4.1-4.3; Figures 5-8; Tables 2-3. canonical-paper-v2-b3cdd293 2d85d87c43a4f79fb0cdf0b8a08c88f9c2a4bee914745e355ded848f3c1347fe exploiting gpus in virtual machine for biocloud b3cdd293# 6c83af46c4473e8a04745812e9a18aff Lesson:technical review exploiting gpus in virtual machine for biocloud 9e8875c0 3000 ko technical review exploiting gpus in virtual machine for biocloud 9e8875c0 Supersedes 102 ko Supersedes