当100万名退伍军人通过参加退伍军人事务部的“百万退伍军人计划”分享他们的基因数据时，他们并未期望得到任何回报。这并未让美国能源部阿贡国家实验室数据科学与学习部门的高级科学家拉维·马杜里感到意外。他非常清楚这些退伍军人捐赠数据时所依据的条件：“即使已经为国家奉献了一切，他们仍发自内心地愿意给予更多，而不期望任何回报。”
When a million veterans shared their genetic data by enrolling in the Department of Veterans Affairs' “Million Veteran Program,” they expected nothing back. This did not surprise Ravi Madduri, a senior scientist with the Data Science and Learning Division at the U.S. Department of Energy’s (DOE) Argonne National Laboratory. He is abundantly clear about the terms under which these veterans donated their data: “Even after giving everything to the country, they found it in their hearts to give more without expecting anything in return.”
这份馈赠构成了全美最大的生物样本库，也是世界上祖先背景最为多样的退伍军人数据队列，由此产生了一份马杜里正在努力偿还的亏欠。他同时在芝加哥大学人类遗传学系担任联合聘任职务。“当我看着这些数据时，我感受到的是，我有义务为退伍军人实现他们这样做的初衷，”他说。按照他的理解，这些初衷是让其他人——无论是否为退伍军人——都能从数据所揭示的一切中受益。
That gift, which constituted the nation’s largest biorepository and most ancestrally varied cohorts of veteran data in the world, created a debt that Madduri, who also holds a joint appointment in the Department of Human Genetics at the University of Chicago, is working to repay. “What I feel when I look at that data is that I have an obligation to deliver to the veterans on their intentions in doing this,” he says. Those intentions, as he reads it, are that others, veteran or not, can benefit from whatever the data can reveal.
“我们不能磨磨蹭蹭，”马杜里说。马杜里于2001年作为研究生来到阿贡国家实验室，此后二十年一直处于超级计算与生物医学的交汇点，大部分时间都在构建将退伍军人数据转化为成果的计算系统，这些成果影响着从创伤后应激障碍到心脏病再到癌症等多个领域。他解释说，机器在很大程度上已经尽到了自己的职责，但从珍贵数据到交付成果之间的障碍，并不总能靠更快的机器来解决。
“We cannot dilly dally,” Madduri says. After arriving at Argonne as a graduate student in 2001, Madduri has spent the last two decades at the intersection of supercomputing and biomedicine, much of the time building the computing that turns the veterans’ data into results impacting areas from post-traumatic stress disorder to heart disease to cancer. He explains that machines have largely held up their end, but the obstacles between precious data and delivered results are not always solved with faster machines.
理解这些挑战，才是通往更好科学的道路。数学所无法解决的问题。任何两个人类基因组在数百万个特定位置上存在差异。全基因组关联研究在庞大的数据量中提出一个看似简单的问题。例如，某个特定的遗传变异是否与某种特定的疾病相伴出现？就其本身而言，这个问题很简单。
Understanding the challenges is the path toward better science. What the Math Was Unable to Solve Any two human genomes differ at millions of specific positions. A genome-wide association study asks a seemingly simple question across an enormous volume of data. For example, does a given genetic variant travel together with a particular medical condition? On its own, the question is simple.
在每一种值得测试的变体以及患者病历中记录的每一种条件上进行乘法运算，这会变成一个巨大的数学难题，而这正是超级计算机的用武之地。更棘手的问题是代码是否被写成可以让它们运行的方式。Madduri 的团队已经在美国能源部橡树岭领袖计算设施的 Summit 系统以及阿贡领袖计算设施的 Aurora 系统上运行了这一问题。
Multiplied across every variant worth testing and every condition recorded in a patient's chart, it becomes an enormous math problem, which is what supercomputers exist to solve. The harder question is whether the code is written to let them. Madduri's team has run this problem on the Summit system at DOE's Oak Ridge Leadership Computing Facility and on the Aurora system at the Argonne Leadership Computing Facility.
在 Summit 上，团队发现该任务在机器的 CPU 上无法完成。随着样本量的增加，分析每个组所需的矩阵不再能装入内存，任务因此卡住。Madduri 说：“我们决定打开盒子查看代码。”他们发现问题不仅仅是速度慢，而是代码本身。该分析最初是为普通处理器编写的，因为遗传学界当时使用的就是普通处理器。然而在算法之下，代码几乎全部时间都在做一件事：对极大的数字网格进行乘法运算。
On Summit, the team found that the job would not finish on the machine's CPUs. As the cohort grew, the matrix the analysis needed for each group no longer fit in memory, and the job stalled. “We decided to open the box and look at the code,” Madduri said. They found that the issue was not merely a speed problem, but a coding one. The analysis had been written for ordinary processors because that is what the genetics community was running on. Underneath the algorithms, though, the code spent nearly all of its time doing one thing: multiplying very large grids of numbers.
这正是图形处理器（GPU）所擅长的操作，也是它们成为现代人工智能训练引擎的原因。正如 Madduri 所指出的，加速人工智能发现的同样计算架构“也是群体基因组学的基础”。于是，由阿贡的 Alex Rodriguez 主导的团队将这部分代码改写为 GPU 版，原本需要约 48.1 万 CPU 核心小时的分析变得可行。这一转变不仅加快了原本缓慢的任务，更使原本几乎不可能完成的任务成为可能。
This is exactly the operation that graphics processors exist to perform, and what makes them the engine of modern artificial intelligence training. As Madduri notes, the same computational architecture accelerating AI discovery “is also foundational for population genomics.”So, the team, led on this effort by Argonne's Alex Rodriguez, rewrote that part of the code for GPUs, and an analysis that would have taken roughly 481,000 CPU-core hours in the original version became tractable. This shift did more than speed up a slow job. It made a virtually impossible job possible.
他们加速的核心工具是广泛使用的遗传学软件包 SAIGE，改写后的版本命名为 SAIGE‑GPU，已在《Bioinformatics》另篇论文中描述。Madduri 羞涩地说：“我们不擅长起名字，抱歉。”在 SAIGE‑GPU 之前，几乎所有全基因组关联研究都是先选定一种疾病，然后寻找与之相关的变体。
The workhorse they accelerated is a widely used genetics package called SAIGE, which made the rewritten version SAIGE-GPU, described in a separate paper in Bioinformatics. "We are not good at coming up with names. Sorry," Madduri says sheepishly. Before SAIGE-GPU, nearly every genome-wide association study started by picking a disease then looking for the variants linked to it.
马杜里对这种做法的成本直言不讳。“我在生物医学研究中遇到的一个挑战是，这项研究进展非常缓慢，几乎是一步一步的，”他解释道。“每年，研究人员都会获得新的数据集，分析这些数据，发现一组新的基因变异，而这种情况在所有不同疾病中都会发生。”他补充说，造成这一问题的原因是大规模计算资源的获取有限。
Madduri is blunt about the cost of that approach. “One of the challenges I found in biomedical research is that it is very incremental,” he explains. “Every year, they get new datasets, they analyze the data, they find a new set of genetic variants, and this happens across all of the different diseases.”The reason, he adds, is limited access to large-scale computing.
他的团队选择不按部就班，而是决定一次性彻底解决计算难题。“我们给自己设定的挑战之一，就是利用所有可用的数据，即‘百万退伍军人计划’框架下已测序的所有退伍军人的基因数据，”马杜里说。该团队——即“百万退伍军人计划”联盟——在《科学》杂志上发表了研究结果，马杜里的团队也是作者之一，而相关的计算工作则单独发表在《生物信息学》杂志上。
His team chose not to be incremental and decided to solve the computational challenge once and for all. “One of the challenges we set for ourselves was to just take all the data that we have available, all the genetic data for all the veterans that have been sequenced as part of the Million Veteran Program,” Madduri says. The team, the Million Veteran Program consortium, published the results in Science, with Madduri's group among the authors, and published the computational work separately in Bioinformatics.
这项研究涵盖了635,969名退伍军人的2,068项特征，结果标识出基因组中13,672个与一种或多种疾病相关的区域。其中约1,600个区域之所以被发现，是因为近三分之一的参与者并非欧洲裔。根据《科学》杂志的论文，迄今为止的大多数遗传学研究几乎都集中在欧洲裔人群身上。这1,600个关联此前在已研究的人群中是“不可见”的。
The study reported on 2,068 traits across 635,969 veterans, and the results flagged 13,672 regions of the genome linked to one or more conditions. About 1,600 of these regions surfaced only because nearly a third of the participants were not of European ancestry. According to the Science paper, most genetic studies to this point have been drawn overwhelmingly from people of European descent. These 1,600 associations were previously “invisible” in the populations studied.
这种祖先群体的多样性也影响了分析方法的执行方式。这些群体无法简单地合并分析。也就是说，如果某种疾病在某个祖先群体中更为常见，分析就无法区分真正的疾病标志物与仅仅属于该群体的标志物。因此，每个群体都必须单独进行分析，最后再将结果合并。这1,600个关联并不存在于任何单一群体的分析结果中，只有在将各组的分析结果合并后才会显现。
That ancestral range also shaped how the analysis had to be run. The groups could not simply be pooled. That is, if a condition is more common in one ancestral group than another, the analysis has no way to tell a genuine disease marker from a marker of simply belonging to that group. Each group had to be run separately, and the results combined afterward. The 1,600 associations exist in no single group's results. They appear only when the separate analyses are combined.
马杜里对这项研究整体的意义及其局限性持谨慎态度。“这一切都还处于临床前阶段，”他说。
Madduri is careful about what the whole exercise means and what it does not. “This is all preclinical,” he says.
“这是发现阶段。”分析会生成一张统计地图，标明应该在哪里寻找。要将这13,672个区域中的任何一个转化为治疗方案，研究人员需要投入数年的后续工作，他们要收集并追踪数据。因此，Madduri表示团队从未停下脚步。“在我们撰写《科学》杂志论文时，某些事情需要14天，而现在只需不到几个小时，”他说。虽然这意味着能更快地获得答案，但真正的突破在于，现在能够针对更大的人群提出同样的问题，“样本规模更大，统计严谨性也比我们几年前发表的论文更高。”
“This is the discovery phase.”The analysis produces a statistical map of where to look. Turning any one of those 13,672 regions into a treatment takes years of downstream work by researchers who pick up and follow the data, which is why Madduri says the team never stopped. “Things that took 14 days when we wrote the Science paper now take less than a couple of hours,” he says. While this means getting an answer sooner, the real win is being able to ask the same question of a bigger population “with a larger cohort size and more statistical rigor than the paper that we published a couple of years ago.”
当数据无法流动时退伍军人基因组数据相较于其他医疗数据具有独特的优势。一家机构通过单一协议收集了这些数据，这也使得将它们带到Summit成为可能。Madduri指出，在医学领域，数据“无法像宇宙学数据那样自由地相互共享”。他解释说，另一种方法是移动模型：“联邦学习让你无需移动数据即可训练模型。”这意味着每个机构都在自己内部持有的数据副本上进行训练，仅将模型学到的调整结果发送回来。这些调整结果会被整合成一个统一的更新模型，再分发给所有参与者，然后循环往复。
When Data Cannot Travel The veterans’ genomes have an unusual advantage over other medical data. One agency gathered the data under one agreement, which is what made bringing them to Summit possible. In medicine, Madduri says, data “cannot be shared with one another as freely as you would probably share cosmology data.”The alternative is to move the model, he says: “Federated learning allows you to train models without moving data around.”This means each institution trains on the copy it already holds, behind its own walls, and only sends back the adjustments the model learned. Those adjustments are combined into a single updated model, which goes out to everyone, and the cycle repeats.
当被问及如何说服多家机构在不共享任何数据的情况下共同计算时，他纠正了问题的前提。“关键不在于机构，”他说，“这个难题的第一层在于人们希望共同工作、相互协作。”接着，他指出了实现这一目标的关键：“这是一个由人组成的联邦。”
Asked how several institutions are persuaded to compute together without pooling anything, he corrects the premise. “It is not the institution,” he says. “The first layer of that puzzle is people wanting to work together, to collaborate.”Then, the part that makes it work: “It is a people federation.”
Madduri是APPFL（阿贡隐私保护联邦学习框架）的联合开发者，该框架提供了一种帮助“移动”模型而非数据的方法。其底层机制更接近于访问控制，而非机器学习。“你无需为每个调度器单独编写代码，”他说。
Madduri was a co-developer of APPFL, the Argonne Privacy-Preserving Federated Learning framework, which provides a means to help “move” models rather than data. The machinery underneath is closer to access control than to machine learning. "You do not have to write code for each scheduler," he says.
Globus Compute为所有计算设施的调度系统提供了一个统一的接口，APPFL通过该接口提交其训练任务。尽管存在这些技术上的挑战，但更棘手的问题在于权限管理。“所有进行的计算活动都必须经过授权，并且这些计算都与特定的项目分配相关联，”Madduri解释道。在分布式训练模式下，每位合作者的任务都必须在他们自己有权使用的机器上，使用他们自己的机构账户来执行。
Globus Compute presents one interface across the facilities' schedulers, and APPFL submits its training jobs through it. Even with these technical challenges, the harder problem is permission. “All the computing that happens is authorized computing that is tied to a project allocation,” Madduri says. Federated training means that each collaborator’s job has to run under their own institutional credentials on a machine they are personally entitled to use.
如果某个站点拥有其他站点所没有的数据，那么这种共享机制对所有人来说都会更加有利。“如果某个站点拥有其他站点没有的独特数据，那么模型的可靠性就会更高，”Madduri补充道。
A site with data nobody else has makes that shared model better for everyone. "If one site has data that are unique to it and other sites do not have that, then the model becomes more robust," Madduri adds.
自2001年在丹佛举行的SC会议（主题为“超越界限”，重点讨论数据跨地域传输的技术）以来，Madduri一直积极参与该会议。当时他还是一名研究生，在美国能源部（DOE）的展位上展示了他的硕士论文成果——该成果利用互联网实现了大量数据的可靠传输。这项技术后来被发展为“Globus”平台。Madduri参与开发的第一个工具用于传输研究数据，而最新的工具则允许数据保留在原始位置（即不需要进行数据迁移）。
Many Different Worlds Madduri has been a regular attendee at the SC Conference since SC01 in Denver, which was themed “Beyond Boundaries,” and focused on moving data across them. “I was a grad student, and I had a demo in the DOE booth demonstrating my master’s thesis, which at that time was to use the Internet to move large amounts of data reliably from one point to the other,” he says. That capability was called reliable file transfer, and, in his words, it “has now transformed into what is called ‘Globus’.”The first tool he helped build moved research data around. The latest one lets the data stay in place. Since SC01, Madduri has been to the conference roughly 20 times and is specific about what he takes home.
自2001年以来，他已参加了大约20次SC会议，并对每次会议收获的知识进行了详细总结：“我学习了并行处理技术、人工智能训练的扩展方法、资源利用的监控机制等等。”他的团队每年都会参加“计算方法在癌症研究中的应用”研讨会。最近，研究热点转向了人工智能代理（AI agents）领域；Madduri认为：“人工智能代理技术是我们未来的发展方向。”
“I learn parallel processing, how people are scaling AI training, how people are dealing with parallelization, how people are dealing with resource utilization monitoring,” he notes. His group attends the Computational Approaches for Cancer Workshop every year. Lately, the interest has moved to AI agents. “The agentic approaches are the next frontier for us,” he declares.
他还意识到自己所处的研究领域非常多样化——在从事生物医学研究之前，他曾与高能物理学家、地震工程师和宇宙学家合作过。“每次参加SC会议，我都仿佛置身于不同的‘研究世界’之中，”他说。
Madduri is also aware that he walks in unusual research domains. Before biomedicine, he worked with high-energy physicists, earthquake engineers, and cosmologists. “When I go to SC, I live in many different worlds,” he says.
“我属于一个非常小的团队——这个团队同时参加‘人类遗传学会议’和‘超级计算会议’。”对于马杜里（Madduri）来说，还有一个问题尚未解决：他的债务至今仍未还清。自从首批退伍军人加入该项目以来，虽然“百万退伍军人计划”（Million Veteran Program）已经收集了大量数据并绘制出了相关图表，但至今仍未找到治愈癌症的方法。
“I am part of a very small group of people that go to a [combined] human genetics conference and a supercomputing conference.”What Is Still Missing For Madduri, his debt is not settled. More than a decade after the first veterans enrolled, the Million Veteran Program data have produced a map but not yet a cure.
阻碍这一目标实现的关键在于如何让不同的研究机构能够协同工作、共享计算资源；目前，这些工作仍主要依赖人工完成。马杜里解释道：“在早期的实验中，我们不得不分别与 NERSC（美国国家能源研究科学计算中心）、阿贡国家实验室（Argonne National Laboratory）和橡树岭国家实验室（Oak Ridge National Laboratory）进行沟通。”
What stands between those two things is the work of getting separate institutions to compute as one. To date, that still is being done largely by hand. “For the earlier experiments, it was us talking to NERSC, us talking to Argonne, us talking to Oak Ridge,” Madduri says. “It is a lot of human in the loop.”
他希望美国能源部（DOE）推出的“美国科学云”（American Science Cloud）能够接管这些管理工作，实现自动化操作（即通过应用程序编程接口 API 来完成各项任务）。目前，马杜里仍致力于将数据转化为实际成果，利用这些数据来推动癌症研究的突破，从而帮助人们摆脱长期的心理焦虑。在他看来，每一次技术进步或每一次超级计算会议的举办，都代表着新的进展。
He is hoping the American Science Cloud being built under the DOE’s Genesis Mission will take that over: “It would be automated, run using APIs.”For now, Madduri remains focused on turning data into triumph, using a very special cohort of data to reveal the next breakthrough against cancer or spare someone a lifetime of anxiety. Until then, he marks progress one good idea, one technical advancement, and one SC Conference at a time.
马杜里表示：“我与超级计算领域的联系由来已久，也取得了丰硕的成果。11月我还会回到芝加哥参加 SC26 大会。”
“My connection to Supercomputing, to SC, is long and very fruitful,” Madduri says. “I will be back in November, in Chicago, for SC26.”
SC26 是一个汇聚了众多关键技术专家的盛会——这些专家致力于构建支撑科学研究的基础架构，包括系统、数据平台、网络和存储设施，这些设施使得跨机构、大规模的研究成为可能。与会者可以参与技术论文讨论、研讨会、培训课程以及专题讨论会（如“AI、量子计算和高性能计算”相关的话题），并与来自国家实验室、工业界和学术界的同行交流经验。
Join the Conversation at SC26 SC26 brings together the people building the computing infrastructure the work described herein depends on: systems, data platforms, networks, and storage that let research run at scale and across institutions. Attendees engage with technical papers, workshops, tutorials, posters, and Birds of a Feather (BoF) sessions covering AI, quantum computing, and HPC alongside practitioners from national laboratories, industry, and academia working on similar problems.
马杜里还将参与 SC26 的区域技术论坛（Regional Tech Hub），在该论坛中他将在“高通量计算基础设施与系统”（High-throughput Computing Infrastructures and Systems）领域发表演讲。
Madduri will also be part of the SC26 Regional Tech Hub, a multimedia forum making its conference debut in Chicago, where he will contribute to the High-throughput Computing Infrastructures and Systems area.
诚邀您于2026年11月15日至20日莅临芝加哥，与我们共度充满探索、合作与交流的一周。本次活动将重点展示各地区的科技领军企业。即刻报名吧！活动信息由SC26提供。
Join us in Chicago, November 15–20, 2026, for a week of discovery, collaboration, and community with a spotlight on regional technology leaders. today! Supplied by SC26