HPCwire Readers' Choice Awards 2026 Survey

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Below you will find the official 2026 Readers' Choice Award ballot. While there are several categories, the entire voting process should take only a few minutes of your time.

All information is kept confidential and is not shared with any third parties. Polling for this ballot closes at 11:59 PM PDT on October 2, 2026, followed by winner notifications.

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1.Best Use of HPC/AI in Life Sciences

The University of Florida’s upgraded fourth-generation HiPerGator, powered by NVIDIA’s Blackwell GPUs with Lenovo servers, is delivering AI for Science at scale. The platform is being used by a number of researchers exploring a variety of issues, including the use of HiPerGator’s computational modeling to design new insulin-sensitizing drugs for Type 2 diabetes. The work could offer better treatment options for patients with diabetes and chronic kidney disease. The system is also being used to simulate extreme events, such as vehicle impacts on bridges or barge collisions with flood-control structures.

NVIDIA developed cuEST, which brings high-accuracy, first-principles quantum chemistry to industrial-scale drug discovery workflows. It does this by accelerating the prediction of molecular properties and promising drug candidates, delivering up to 50x speedups over traditional CPU methods. The solution enabled life sciences and drug-discovery researchers to run high-accuracy, first-principles quantum chemistry simulations to assess candidate compounds and materials with molecular discovery and design, property prediction, and reaction modeling earlier in R&D.

To better understand the loss of vision caused by space flight, University of California San Diego researchers used U.S. National Science Foundation ACCESS allocations on the Expanse system at the San Diego Supercomputer Center. The team trained AI on high-resolution eye scans to predict individuals at highest risk to support risk management and, in the future, preventative measures prior to launch. That work allowed them to predict who is most at risk for developing eyesight issues before liftoff.

NVIDIA’s cuEquivariance library, whose drug discovery kernels were architected by Dr. Hari Sadasivan, delivered GPU-optimized equivariant neural network primitives. The primitives are upstreamed into nearly every leading structure prediction and molecular design model — including RoseTTAFold3, OpenFold3, Boltz-2, and Protenix — making state-of-the-art drug discovery AI dramatically faster and cheaper across the life sciences.

Memorial Sloan Kettering used Amazon Bio Discovery to generate more than 288,000 candidate nanobody sequences against a novel pandemic-relevant viral protein target in a single computational run. The work replaced months of traditional wet-lab screening with days of AI-driven protein design on AWS cloud infrastructure. It compressed what traditionally takes 6–12 months of camelid immunization and phage display screening into mere days.

MindWalk’s LensAI screened more than 890,000 antibody sequences for immunogenicity risk in four hours and 41 minutes on AWS Hpc8a instances powered by 5th Gen AMD EPYC processors. The work had previously taken weeks. This speedup allowed MindWalk to move immunogenicity from a late-stage clinical risk into a front-of-pipeline filter that eliminates high-risk candidates before a single molecule is synthesized.

Researchers from Auburn University used NCSA’s Delta supercomputer to combine AI structure prediction with extensive GPU-accelerated steered molecular dynamics simulations to discover the strongest noncovalent biomolecular interaction ever recorded. By revealing how mechanical stress and disrupted calcium gradients hyper-stabilize bacterial skin adhesion during eczema flares, this HPC and AI-driven discovery opens critical pathways for developing novel non-antibiotic therapeutics and targeted molecular inhibitors against Staphylococcus aureus skin colonization.

University of Birmingham researchers, in partnership with Johnson & Johnson, analyzed more than 5TB of data and leveraged over 400 CPU hours. The work delivered the world’s first clinical trial to use single-cell RNA sequencing as a primary outcome to evaluate the efficacy of a novel therapeutic agent across rheumatoid arthritis, Sjögren’s disease, and ulcerative colitis.

Chelatron combined NVIDIA Nemotron LLMs, GenMol NIM and NeMo software, Academy and Parsl orchestration, LANL’s Architector, CMU’s LoQI, Gaussian-process surrogate models, and xTB/ADF quantum chemistry together into NERSC’s Perlmutter supercomputer application. The solution allowed Chelatron to identify 679 candidates predicted to bind actinium as well as or better than the leading candidate, and created an experimentally actionable pipeline that could accelerate safer and more effective actinium-225 targeted alpha therapies for cancer.

A team from the City University of New York used a D. E. Shaw Research Anton supercomputer hosted at the Pittsburgh Supercomputing Center to show how some cancer mutations increase gene accessibility, which may affect cancer progression. That work was based on the use of the second-generation Anton supercomputer developed by DESRES, a unique molecular dynamics simulation machine that can produce simulations up to 10 to 100 times longer than those possible with general-purpose supercomputers.

Sanofi built SWEL (Scientific Workflow Experience Labs) on AWS to give over 2,000 bench scientists self-service access to AI-driven drug discovery tools without writing code, connecting generative chemistry, molecular simulation, and literature mining into a single orchestrated platform that runs on elastic GPU compute. SWEL enabled Sanofi to run two to three times more drug discovery projects in parallel while cutting individual experiment turnaround from weeks to days, resulting in 10x faster deployment.

St. Jude Children’s Research Hospital used Private Cloud AI (PCAI), a solution by HPE, to host eight domain-specific NVIDIA BioNeMo NIMs, spanning genomics, structural biology, and protein design. The group built a custom NIM Router that abstracts away the inconsistent request/response contracts so any client can call any model through one consistent interface. The team also built a modified BioNeMo Skills integration that routes natural-language requests from a researcher’s own agent of choice (Cursor, Claude, Codex) straight to PCAI.

Insilico Medicine used its generative AI platform Pharma.AI to discover a novel biological target (TNIK) and design a novel small-molecule drug (Rentosertib) entirely through AI. The platform runs the large-scale workloads on AWS Batch, allowing the company to advance the drug to Phase III clinical trials in July 2026 for idiopathic pulmonary fibrosis (IPF), and making it the first drug in history with both an AI-discovered target and an AI-designed molecule to reach late-stage clinical development.

Researchers at the Advanced Research Computing at the University of Birmingham developed a computer vision pipeline to accurately detect, segment, and track rapidly swimming sperm to improve male infertility diagnosis. This project combines fine-tuned AI computer vision models (YOLO and RF-DETR) for small-object instance segmentation and multi-object tracking with parallel processing on the University of Birmingham’s BlueBEAR HPC cluster (Lenovo and IBM Storage Scale), using Intel CPUs and NVIDIA A100 GPUs to efficiently analyze terabytes of high-framerate microscopy data.

5.Best Use of HPC/AI in Financial Services

Jump Trading used VAST to give thousands of CPUs and GPUs shared, high-performance access to quantitative research data, eliminating data staging and replication while dramatically increasing throughput. Using this technology, Jump achieved more than 6x its previous read performance and cluster throughput hit 1.25 TB/s at approximately 0.6 ms latency, while achieving roughly 2:1 data reduction.

Revolut, in collaboration with NVIDIA, developed PRAGMA, a family of Transformer-based foundation models (10M to 1B parameters) pre-trained on 24 billion banking events. PRAGMA was trained on clusters of NVIDIA H100 GPUs and replaces fragmented, task-specific financial models with a single unified backbone that delivers state-of-the-art performance across credit scoring, fraud detection, and more.

Inshurik ran its fully agentic voice-to-SurePath bind workflow on AWS Bedrock to power tens of thousands of compliant customer interactions at scale, making a single licensed advisor 10 to 50 times more productive while staying inside strict NAIC and state regulatory requirements. This allows the company to scale from thousands to tens of thousands of compliant customer interactions with only marginal headcount growth.

Researchers at the University of Bristol’s Bristol Centre for Supercomputing developed and evaluated more than 600 AI models on their ability to predict financial returns under time-aware conditions. Running on HPE systems, these models produced more reliable return forecasts that gave investors and institutions clearer proof of which AI models can be trusted in real markets.

A team led by Carnegie Mellon University and the Pittsburgh Supercomputing Center developed a new approach called POPri (Preference Optimization for Private Client Data), which generates safe synthetic data to train large language models without exposing private user information. The work allows researchers to personalize LLMs to users’ preferences, avoiding potentially exposing financial and other data to bad actors.

7.Best Use of HPC/AI in the Cloud (Use Case)

Scaler allows users, including software engineers, quantitative modelers, and analysts, to write code in its natural form, and it automatically parallelizes it and scales it from local machines to HPC Grids (Symphony) and the Clouds. Additionally, Scaler allows HPC jobs to be automatically routed to multiple compute pools and scaled based on user policies, including cost, data governance, or latency. The net result is increased utilization of on-prem resources and lets users treat on-prem and cloud as one seamless fabric, allowing on-prem jobs to burst to the cloud.

Chipshub delivers advanced semiconductor design and simulation tools through a scalable HPC-powered cloud platform, enabling hands-on workforce development and broad access to industry-grade electronic design automation capabilities for students and researchers nationwide. To do this, it leverages the Anvil Kubernetes-based composable system to dynamically scale semiconductor design and simulation workloads, enabling hundreds of concurrent users to access advanced EDA tools through a web browser.

The National Oceanic and Atmospheric Administration (NOAA) leveraged Google Cloud to modernize the Weather and Climate Operational Supercomputing System. The work represents a milestone as one of the world’s first operational numerical weather prediction centers to transition primary forecasting infrastructure to the public cloud, setting a new benchmark for scalable, high-performance computing in atmospheric science.

The San Diego Supercomputer Center (SDSC) partnered with CENIC on a project called CENIC AIR, a distributed, shared AI computing infrastructure spanning more than 20 California campuses, to give community colleges and California State Universities access to a dedicated AI infrastructure. CENIC AIR pools 1,044 GPUs, 14,604 CPU cores, and over ten petabytes of storage across campus-owned hardware, with SDSC and CENIC centrally handling operations, security, and support in a cloud-provider-style model.

Salesforce Research used Google Cloud Managed Lustre powered by DDN EXAScaler to eliminate data bottlenecks when training large-scale AI models on GPU clusters and improve the efficiency of its cloud-based training infrastructure. The deployment provided massively parallel data throughput for Salesforce’s GPU training environment, reducing I/O latency by 75%, accelerating model training by 1.5x, and increasing GPU utilization by 70%.

AWS collaborated with the National Nuclear Security Administration (NNSA) and its Genesis Mission Teams from Lawrence Livermore, Sandia, and Los Alamos National Laboratories to deliver a hybrid cloud AI capability for the Nuclear Security Enterprise. The effort connects LLNL’s El Capitan exascale supercomputer with frontier AI models through Amazon Bedrock running in NNSA’s first-ever enterprise-authorized cloud environment for Secret/Restricted Data, a seminal public-private partnership to address the nation’s most pressing national security challenges.

Toyota’s European organizations (Toyota Motor Europe and Toyota Gazoo Racing Europe) migrated their full HPC simulation and AI training environments to AWS, achieving 20% faster ML model training for robotics, 80% fewer days to train proprietary models, 21% faster crash simulations, 10% faster CFD processing, and 95% lower carbon footprint, transforming vehicle development timelines from weeks to days. Toyota’s European teams now deliver safety features, AI-powered driving assistance, and race-winning aerodynamic designs in days instead of weeks.

Zimmer Biomet, AWS, Invisibl Cloud, Dassault Systèmes, and Synopsys ran an in silico clinical trial for a total shoulder replacement implant that required three months and 3,000 finite element models on-premises and ran more than 46,000 virtual surgeries in a single 24-hour window with HPC on AWS. This workload computed overnight, at more than 1,000 times the on-premises throughput while reproducing the validated on-premises results to within 1%, thus accelerating the computational portion of evidence from medical devices.

8.Best QC Integration with HPC/AI

IonQ, AstraZeneca, AWS, and NVIDIA built an end-to-end hybrid quantum-classical drug development workflow on Amazon Braket that achieved a 20x speedup over prior benchmarks for pharmaceutical reaction simulation. The work represents one of the first demonstrated cases where quantum computing outperforms key classical methods for a real-world industrial application.

At Lawrence Livermore National Laboratory (LLNL), researchers are combining quantum computing concepts with classical high-performance computing. They use El Capitan, the world-class exascale supercomputer, to run complex atomic-scale simulations while offloading heavy quantum calculations to external quantum frameworks. The work relies on combining the power of the El Capitan supercomputer, specialized superconducting circuits, and advanced quantum sensing devices tailored specifically for hybrid integration with supercomputers.

NVIDIA Ising Calibration, an open-source 31-billion-parameter vision-language model, runs in an agentic workflow hosted locally to provide control and flexibility on both HPC-scale and prototype-level classical and quantum hardware, for example, on an NVIDIA DGX Spark, as used by Aegiq. It is in use at Fermilab, LBNL, Sandia, Atom Computing, IQM, Infleqtion, and many others, including Aegiq’s agentic workflow that uses the NVIDIA Ising Calibration vision-language model to achieve a 3x speedup in the calibration of its Artemis photonic quantum processor at the UK’s National Quantum Computing Centre.

The Quantum Resource Management Interface (QRMI) integrates heterogeneous quantum processors from vendors including IBM, Pasqal, and Alice & Bob into HPC environments through a common, vendor-neutral interface demonstrated across Slurm, PBS, IBM LSF, Open Cluster Scheduler, Kubernetes, and the Flux Framework. It is an open-source project that reduces the cost and complexity of quantum-HPC integration by allowing HPC centers to support multiple QPU (quantum processing unit) providers through their existing workload managers.

CSC (IT Center for Science) is establishing the LUMI AI Factory as a European hub for hybrid computing by tightly integrating quantum processors directly with the LUMI supercomputer. The system uses the IQM Halocene H4 on-premises superconducting processing unit, and the supercomputing engine is an HPE Cray EX, handled by LUMI’s GPU partition, consisting of AMD Instinct MI250X GPUs.

12.Best HPC Interconnect Product or Technology

13.Best HPC/AI Cloud Platform

Amazon Web Services

SDSC and MCNC, the nonprofit operator of North Carolina's research and education network, along with DataMorph

Google Cloud Platform

RACE (RMIT Advanced Computing Ecosystem): Australia's first dedicated commercial cloud supercomputing facility at a university

Parallel Works ACTIVATE AI Gateway

IREN: Canada's Lenovo and NVIDIA-based AI cloud

CoreWeave running VAST AI OS

16.Top Energy-Efficient HPC/AI Achievements

The latest release of cuBLAS, a GPU-accelerated implementation of the standard Basic Linear Algebra Subprograms (BLAS) library developed by NVIDIA, introduced new floating-point techniques (Ozaki-II, 2M methods) that enable the execution of FP64 matrix computations on energy-efficient, low-precision Tensor Cores without sacrificing numerical accuracy at factors faster than silicon execution alone. Using the Rubin generation of NVIDIA GPUs, the Double-precision General Matrix Multiply (DGEMM) and ZGEMM achieved 200 TFLOP/s and 380 TFLOP/s respectively (1-GPU).

Researchers at Sigma2, Norway’s national provider of HPC and large-scale data storage for research and higher education, used an HPE Cray EX supercomputer to help Norway produce more renewable electricity from existing water resources through large-scale scenario modeling. The effort produced more power from its existing plants without building new dams or tapping additional natural resources to minimize environmental impact.

AWS Trainium chips are purpose-built silicon that avoids the power overhead inherent in general-purpose GPU architectures. Instead of relying on NVIDIA GPUs, a cluster built entirely from Amazon’s custom-designed Trainium silicon uses less power. At Rainier’s million-chip scale, the savings using Trainium3 mean the same training workload draws roughly one-quarter the power of its Trn2 predecessor, saving hundreds of megawatts annually and establishing custom AI silicon as an energy-efficient path to frontier model training.

A new California Energy Commission project that encompasses the San Diego Supercomputer Center (SDSC), could change how AI data centers receive electricity while helping prevent them from overwhelming California’s power. The group undertook a demonstration that aims to cut AI data center energy waste by roughly 25%, shrink power equipment footprints by more than half, and generate real-world data to help California utilities and policymakers plan for surging AI power demand.

Over the past year, DreamWorks Animation completed a multi-year transformation to a fully water-cooled, 50,000-core production rendering environment powered by Lenovo Neptune liquid cooling, creating one of the entertainment industry’s most advanced and energy-efficient HPC infrastructures. The deployment increased rendering compute capability by 20% while operating within the constraints of an existing data center footprint, enabling DreamWorks to support increasingly complex productions without expanding facility space. By leveraging Neptune’s warm-water cooling architecture, DreamWorks reduced annual data center power costs by 10%.

KAIROS, the BullSequana XH3000 supercomputer operated by CALMIP at the University of Toulouse–CNRS, ranked No. 1 on the June 2026 Green500 with 73.28 GFLOPS/W on the HPL benchmark. Its direct-liquid-cooled NVIDIA Grace Hopper GH200 architecture pairs benchmarked efficiency with practical research capability, including up to a 30% reduction in compute-hours for selected decarbonization models.

The Energy Aware Runtime (EAR) for dynamic energy optimization and smart power capping in large-scale HPC and AI clusters was deployed across Tier-0 supercomputers including MareNostrum 5 and SuperMUC-NG. EAR optimizes over 21,500 servers and 22 MW of compute capacity to deliver up to 20% overall energy savings and reduce peak power draw by up to 33% with zero application code changes.

The HLRS (High-Performance Computing Center Stuttgart) modernized critical high-performance computing environments while taking a more sustainable approach to technology lifecycle management. By maximizing the value of existing infrastructure and accelerating deployment of next-generation systems, HLRS doubled power output while dramatically reducing energy consumption.

19.Outstanding Leadership in HPC/AI (Please select top choice and add a 2nd choice in comment field below)

Matt Garman, CEO, AWS

Matt Garman, CEO of AWS, announced in November 2025 a $50 billion investment in purpose-built federal AI and supercomputing infrastructure across all classification levels (Top Secret, Secret, and GovCloud) spanning custom Trainium silicon, NVIDIA GPU partnerships, global data center expansion, and the Anthropic strategic relationship. This represented the largest private-sector infrastructure commitment to federal AI and HPC in history and will fundamentally transform how federal agencies leverage supercomputing.

Frank Würthwein, Director, San Diego Supercomputer Center (SDSC)

Frank Würthwein, Director of the San Diego Supercomputer Center (SDSC), helped bring SDSC’s advanced computing expertise to the F3i Supercomputing Center, a partnership among F3 Innovate, Fresno State, and SDSC. The center expanded access to HPC, AI expertise, and workforce training for California’s Central Valley agriculture and food sector. Drawing on SDSC’s leadership with the National Research Platform, Würthwein is helping connect agricultural researchers, growers, startups, and students with the computing, data, and networking resources needed to address real-world challenges.

Elizabeth Leake, Founder and President, STEM-Trek

Elizabeth Leake is a project manager for Advanced Cyberinfrastructure IV at Texas A&M University. She founded STEM-Trek, a global, grassroots nonprofit organization that supports mentoring and professional development opportunities for science, technology, engineering, and mathematics scholars from underrepresented groups and regions. Additionally, the group helps young professionals and researchers in HPC to advance their careers on a global stage by financing their travel and participation in leading international conferences. STEM-Trek is solely supported through volunteer efforts.

Gilad Shainer, Senior Vice President of Networking, NVIDIA

Gilad Shainer, Senior Vice President of Networking at NVIDIA, is one of the most influential voices at the convergence of HPC and AI infrastructure, spending 2024-2025 simultaneously leading the development and industry adoption of networking platforms that are powering frontier scientific computing and the AI factories that are redefining what large-scale computation can achieve.

Gary Grider, Senior Director for Computing Technologies, Los Alamos National Laboratory

Gary Grider, Senior Director for Computing Technologies at Los Alamos National Laboratory and for many years its HPC Division Leader, has spent four decades solving the single hardest problem in HPC storage as co-inventor of the burst buffer and PLFS as well as a driving force behind pNFS and Lustre at scale. He was also the originator of GUFI. Over the past year, that work has entered its most consequential phase, with GUFI now being absorbed into commercial storage and AI products.

Brian Sparks, Sr. Director of Marketing, NVIDIA

Brian Sparks, Director of Marketing, NVIDIA, has distinguished himself as one of the HPC community’s most dedicated advocates, cultivating the next generation of HPC and AI practitioners through the annual ISC Student Cluster Competition, while simultaneously expanding global community engagement through HPC-AI Advisory Council workshops and driving industry alignment across the InfiniBand Trade Association, Ultra Ethernet Consortium, and beyond.

Dan Stanzione, Executive Director, Texas Advanced Computing Center (TACC)

Dan Stanzione, Executive Director of the Texas Advanced Computing Center (TACC), exemplifies outstanding leadership in HPC and AI through his vision, technical expertise, and sustained commitment to making advanced computing a transformative public resource. As Executive Director of TACC, he has built and led a nationally significant computing ecosystem that enables researchers across disciplines to tackle grand scientific challenges with HPC and AI. On behalf of TACC and the NSF Leadership Class Computing Facility (LCCF), Dan has played a pivotal role in advancing the infrastructure, partnerships, and research community needed to deliver the next generation of computing capabilities at national scale.

Please enter your second choice here

20.Workforce Diversity & Inclusion Leadership Award

NCSA built its Delta supercomputer to make high-performance computing far more accessible to researchers with disabilities. Teaming up with University of Illinois partners, NCSA hosts open workshops that walk through screen-reader workflows and non-visual interaction strategies for cluster environments. To support blind and visually impaired individuals specifically, the group develops tactile, multi-sensory STEM tools and accessible coding interfaces. Together, these efforts help build a clear, supportive path for marginalized talent from early education into advanced scientific computing.

The Advanced Computing Student Collaborative is a workforce development initiative that brought together 26 undergraduate students from diverse disciplines to develop computational and data analytics skills and apply advanced computing to real-world challenges. The initiative was hosted by Chaminade University of Honolulu and sponsored by the National Science Foundation (NSF) TACC Frontera supercomputer award. Participants represented colleges and universities across the Pacific and the U.S., including Guam, Micronesia, American Samoa, the Marshall Islands, Hawaiʻi, Alaska, Louisiana, Virginia, North Carolina, Maryland, and Texas.

AWS’s $100 million Education Equity Initiative empowers organizations worldwide to build and scale cloud- and AI-powered digital learning solutions for underserved learners, from early literacy through workforce development. The program provides AWS credits so eligible customers focused on advancing opportunities for underserved communities can access cloud technology and AI services.

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