블로그 글 추가: 2026-06-01-how-linkedin-uses-pytorch-extreme-scale-optimization, LinkedIn은 PyTorch로 어떻게 극단적 규모의 최적화 문제를 푸는가#92
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…tion, LinkedIn은 PyTorch로 어떻게 극단적 규모의 최적화 문제를 푸는가
PR Preview빌드가 완료되었습니다! 아래 링크에서 변경사항을 확인할 수 있습니다. 미리보기: https://pytorchkr-pr-preview-92.surge.sh
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번역 글 소개
LinkedIn은 PyTorch로 어떻게 극단적 규모의 최적화 문제를 푸는가 번역 글을 추가합니다.
LinkedIn이 Scala/Spark 기반의 CPU 중심 분산 선형 계획법(LP) 솔버 DuaLip을 GPU로 가속한 PyTorch 버전(DuaLip-GPU)으로 재설계한 사례 연구입니다. 수억 명의 사용자와 수조 개의 결정 변수를 다루는 극단적 규모의 LP를 희소 텐서 연산, 배치 사영 커널, all-reduce·broadcast 기반 분산 최적화로 구현하여 CPU 대비 자릿수 단위의 속도 향상(8 GPU에서 반복당 75배)과 거의 선형적인 멀티 GPU 확장을 달성했습니다.