匹茲堡大學研究員致力於”GPU圖形處理器加速達數百倍快” (Several hundredfold)的分子動力學研究
約書亞研究藉由GPU圖形處理器高速運算的威力, 加速進一步了解例如糖尿病與癲癇等常見疾病的生物現象. 我們是從匹茲堡大學的Grabe Lab知道了他的研究, 並特別感興趣於他的研究居然能獲得FundScience, 一個專門挹注資金於獲得同行互評為前瞻研究以期達到從大眾取經效果的非營利性組織, 這樣一流的公眾科研投資單位挹注資金, 真不簡單. 我們找到了約書亞, 並做了個簡短的訪問:
Joshua Adelman is studying biological phenomena to better understand common diseases such as diabetes and epilepsy, leveraging the computational power of GPUs. We recently discovered his work through the Grabe Lab at the University of Pittsburgh, and were particularly interested when we learned he’s funding his project through peer-reviewed, “crowd-source” funding organization, FundScience. We caught up with Joshua, and had a chance to interview him to learn more:
NVIDIA: 約書亞, 能告訴我們你的研究方向嗎?
NVIDIA: Joshua, tell us about your research.
約書亞: 我研究細胞內蛋白質如何有選擇性的通過細胞膜傳遞微小分子. 特別來說, 我研究兩種物質傳遞 – 一種是神經遞質(神經傳遞物質), 這是透過突觸傳遞對於特定神經系統的關鍵作用; 而另一種是腸道與腎臟如何進行吸收糖分子的吸收. 這兩種研究都與肌肉萎縮症(ALS), 癲癇, 與第二型糖尿病未來的新治療方向有關係.
Joshua: I am interested in understanding how proteins that sit in the cell membrane selectively transport small molecules across the membrane. Specifically, I study two transporters – one removes a neurotransmitter from the synapse and is critical in proper nervous system function; the other is responsible for absorption of sugar in the intestines and kidneys. Both are potential targets for treating a number of diseases including ALS, epilepsy and type 2 diabetes.
Joshua Adelman (seated) with advisor Michael Grabe
NVIDIA: 你如何使用GPU圖形處理器高速運算?
NVIDIA: How are you leveraging GPU computing?
約書亞: 我用分子動力學進行蛋白質模擬, 以了解每個幫助物質傳遞方案所產生的結構性變化. 已經有很多用GPU圖性高速運算於蛋白質研究的例子, 證實了這個方法前途有望. 我只是站在這個基礎上, 用了OpenMM API, 這個用NVIDIA CUDA與OpenCL高速平行運算的應用程式介面, 以GPU圖性高速運算做到分子動力學模擬所需要的各種演算.
Joshua: I use molecular dynamics to simulate these proteins to understand how structural changes in each facilitate transport. There has been considerable effort over the last couple of years by several groups to use GPUs to accelerate these types of calculations. The initial results are quite promising. I’m piggybacking off of one of these efforts, the OpenMM API which provides CUDA and OpenCL implementations of key algorithms necessary to run molecular dynamics simulations. Molecular model of a glutamate transporter
NVIDIA: 那麼你達成了什麼結果?
NVIDIA: What kind of results are you seeing?
約書亞: 使用GPU高速平行運算, 即使在簡單的蛋白質表現的重建模擬上, 我們也能獲得驚人的, 數百倍的加速 – 相較於使用單核CPU的序列運算(*Note: CPU是序列運算, 再多核也不能平行處理, 所以才說與單核比較). 在這點上, NVIDIA CUDA GPUs高速平行運算已經是一種賦能技術(*Note: 能因此種技術而達成終端研究的一種中介的關鍵促成技術). 它讓我們可以做到前幾年的科技所無法做到的高速運算量.
Joshua: For simple representations of the protein, we typically get a several hundredfold increase in simulation throughput compared to a CPU implementation running on a single core. In this regard, GPUs running CUDA have been an enabling technology. They have allowed us to perform calculations that would have been completely unfeasible just a couple of years ago.
NVIDIA: 請告訴我們你的研究如何獲得資金的挹注?
NVIDIA: Tell us about how your project is funded.
約書亞: 我們部分資金來自於FundScience, 一個專門挹注資金於獲得同行互評為前瞻研究以期達到從大眾取經效果的非營利性組織. 我的研究在FunScience主持研究計畫中排名前三大重點項目, 而這筆先期資金挹注的確有效的幫助, 並開始與維持我這個研究計畫.
Joshua: We are funded in part through FundScience, a non-profit organization that financially supports peer-reviewed pilot projects using crowd-sourced funding. My work was selected as one of the first three projects hosted on their site and the preliminary support has been a real boon in getting this project up and running. NVIDIA: 在你的研究計畫上, 誰提供了貢獻與成為你的研究夥伴?
NVIDIA: Who are some of your contributors and partners?
約書亞: CDW與Paragon Micro兩家公司慷慨的捐獻以CUDA為基礎的NVIDIA GPU圖形處理器, 並且本計畫也獲益於許多對我們感興趣的學術個體. 我也獲得匹茲堡大學模擬與建模中心(University of Pittsburgh’s Center for Simulation and Modeling)莫大的協助, 匹茲堡大學模擬與建模中心的策略方向是, 成為一個致力於使用高等通用型圖形處理器運算(Advanced GPGPU)以幫助與訓練各種科學領域獲得GPU高速運算能力的專門中心.
Joshua: CDW and Paragon Micro have generously donated CUDA-based GPUs and the project has been supported by individuals with an interest in the science. I also have worked extensively with the University of Pittsburgh’s Center for Simulation and Modeling, which has made a strategic commitment to advancing GPGPU computing in various scientific disciplines on campus.
NVIDIA: 人們如何更進一步知道你的研究內容, 與如何提供協助?
NVIDIA: How can people learn more about your research and how to donate?
