Rescale 고객: University of Kansas
Kansas 대학에서 생명공학 분야에서 나노유체학 연구를 하는 연구원으로서 나노 습윤성 분석에 필요한 방대한 데이터를 다루기 위하여 대규모의 컴퓨터 리소스를 필요로 합니다. 연구원인 Eric Lee가 오픈 소스 분자역학 분석용 LAAMPS를 사용함으로서 달성할 수 있었던 것은 아래와 같습니다.
런타임 단축
런타임을 4일에서 12일 미만으로 대폭 단축
확장성
Rescale의 전문적인 기술팀으로부터 시뮬레이션 설정을 위한 신속한 기술 지원을 받습니다
효율성
HPC 리소스를 특정 분자 시뮬레이션 요구에 맞게 사용자 지정
생명과학 산업 전문가를 위한 HPC 자료 보기
AZothBio Case Study – Korean
Case Study – AZothBio Accelerates New Drug Discovery Using Deep Learning on Rescale
“Working with strong collaborators like Rescale and Microsoft, Bionano is driving further improvements in sample-to-answer times and costs to address our customers’ needs to run more samples and study more diseases. Our mission is to help improve clinical outcomes.”
Mark Oldakowski, COO
Bionano
“We ran a few thousand test simulations and then scaled up to millions of test simulations running on Rescale. We were able to integrate proprietary code bases seamlessly, quickly identify errors within the scaling code, track each error down, and correct each error using Rescale much more quickly than we were able to run simulations on our own”
Mike Gahan, Senior Data Scientist
Metabiota
“Rescale has definitely given us speed. Earlier, it used to take us somewhere between 5-6 months to do some computations and we would ideally do it on a very late-stage asset as a gut check. Now we are moving towards applying it early in the pipeline. Meaning applying it early enough to weed out molecules that may not work out later. Putting a $ value is tough on last one but you can imagine a lot of developmental time, resources and material saved if you know early on that certain molecule may not work.”
Kushal Sinha, Group Lead, Cross-Functional Model Forum, Process R&D
“I was very impressed with the runtime performance. This massive productivity gain helped my research efforts tremendously.”
Kushal Sinha, Group Lead, Cross-Functional Model Forum, Process R&D