ACCESS MATCH Services connects researchers with consultants, mentors, and students to help solve their research problems.


Help from a student/mentor team
3-6 month engagement

We will match you with a support team based on your needs.

Solve research needs like expanding code, transitioning from lab computers to HPC, or introducing new technologies.

Steps in a MATCH Plus Engagement


Researcher (PI) recognizes support need and submits an engagement request.


A mentor and a student are matched with the project.


Student carries out the project with a mentor and PI through regular meetings.


How can I find out if my project is a good fit for a MATCH engagement?
Fill in a MATCH Request Form or contact to discuss your research need.
What’s the timeline for a MATCH Plus engagement?
Once an engagement is received it often takes 2-4 weeks to process, scope, and staff a request. MATCH Plus engagements typically occur during the schedule of a semester.
What are example Milestones in a MATCH Plus engagement?
Example Milestones in a 3-4 Month Engagement
  1. Understand code/research problems
    • Identify CPU & memory requirements
  2. Profile code
    • Identify hot spots
    • Outline plan for improvements
  3. Apply for ACCESS resources
    • Identify appropriate resources
    • Create estimation of SUs required based on testing and profiling.
  4. First round of implementation of improvements
  5. Github repository made public
    • Documentation complete
    • Public wrap presentation
Example Milestones in a 6 Month Engagement
  1. Ramp Up
    • Discuss recurring meeting schedules (weekly, monthly, etc.)
    • Tutorials (Intro to GPU computing)
    • Jetstream accounts setup, access/navigation
    • Test run ice sheet flow simulations on GPUs, view results
  2. Identify and rank top 1-3 bottlenecks
  3. Alleviate top 1-3 bottlenecks
  4. Develop a performance portable hardware (or architecture) agnostic implementation
  5. Wrap up
    • Prepare conference poster/proceeding
    • ACCESS Wrap meeting/presentation
How is a student and mentor matched to an MATCH Plus Engagement?
Once an engagement is received by the MATCH staff and deemed a viable engagement, the researcher fills in details about the engagement that help match a student and mentor to the project. CSSN members maintain their expertise and interests with tags and the MATCH team uses that information to find the best candidates for an engagement.
How much time commitment is involved for students?
Time commitment varies per engagement need but a typical time commitment is 5-10 hours a week for 3-6 months.
How much time commitment is involved for mentors?
Time commitment varies per engagement need but a typical time commitment is 2-3 hours a week for 3-6 months.

MATCH Premier

Help from an expert consultant
6-12 month engagement
Funded by your institution or your grant funds

Use expert consultants to help with your compute research challenges.

Proposal support may be available if you need significant or unusual allocations, including projects where a well designed plan will maximize scientific output.

Steps in a MATCH Premier Engagement


Researcher submits a project and secures funding.


Consultants are matched with the projects and payment is arranged.


Consultant carries out project with PI.

MATCH Engagements

Find out about the ACCESS MATCH Engagements and if you want to get involved use the “I’m interested” button on recruiting engagements.

GPU-accelerated Ice Sheet Flow Modeling
University of North Dakota

<p>Sea levels are rising (3.7 mm/year and increasing!)! The primary contributor to rising sea levels is enhanced polar ice discharge due to climate change. However, their dynamic response to climate change remains a fundamental uncertainty in future projections. Computational cost limits the simulation time on which models can run to narrow the uncertainty in future sea level rise predictions. The project's overarching goal is to leverage GPU hardware capabilities to significantly alleviate the computational cost and narrow the uncertainty in future sea level rise predictions. Solving time-independent stress balance equations to predict ice velocity or flow is the most computationally expensive part of ice-sheet simulations in terms of computer memory and execution time. The PI developed a preliminary ice-sheet flow GPU implementation for real-world glaciers. This project aims to investigate the GPU implementation further, identify bottlenecks and implement changes to justify it in the price to performance metrics to a "standard" CPU implementation. In addition, develop a performance portable hardware (or architecture) agnostic implementation.</p>

Status: Finishing Up
Investigation of robustness of state of the art methods for anxiety detection in real-world conditions
University of Illinois at Urbana-Champaign

<p>I am new to ACCESS. I have a little bit of past experience running code on NCSA's Blue Waters. As a self-taught programmer, it would be interesting to learn from an experienced mentor.&nbsp;</p><p>Here's an overview of my project:</p><p>Anxiety detection is topic that is actively studied but struggles to generalize and perform outside of controlled lab environments. I propose to critically analyze state of the art detection methods to quantitatively quantify failure modes of existing applied machine learning models and introduce methods to robustify real-world challenges. The aim is to start the study by performing sensitivity analysis of existing best-performing models, then testing existing hypothesis of real-world failure of these models. We predict that this will lead us to understand more deeply why models fail and use explainability to design better in-lab experimental protocols and machine learning models that can perform better in real-world scenarios. Findings will dictate future directions that may include improving personalized health detection, careful design of experimental protocols that empower transfer learning to expand on existing reach of anxiety detection models, use explainability techniques to inform better sensing methods and hardware, and other interesting future directions.</p>

Status: Complete
Adapting a GEOspatial Agent-based model for Covid Transmission (GeoACT) for general use
University of California San Diego

<p>GeoACT (GEOspatial Agent-based model for Covid Transmission) is a designed to simulate a range of intervention scenarios to help schools evaluate their COVID-19 plans to prevent super-spreader events and outbreaks. It consists of several modules, which compute infection risks in classrooms and on school buses, given specific classroom layouts, student population, and school activities. The first version of the model was deployed on the Expanse (and earlier, COMET) resource at SDSC and accessed via the Apache Airavata portal ( The second version is a rewrite of the model which makes it easier to adjust to new strains, vaccines and boosters, and include detailed user-defined school schedules, school floor plans, and local community transmission rates. This version is nearing completion. We’ll use Expanse to run additional scenarios using the enhanced model and the newly added meta-analysis module. The current goal is to make the model more general so that it can be used for other health emergencies. GeoACT has been in the news, e.g.&nbsp;<a href="… San Diego Data Science Undergrads Help Keep K-12 Students COVID-Safe</a>, and&nbsp;<a href="… Supercomputers Helped Enable Safer School Reopenings</a>&nbsp; (HPCWire 2022 Editors' Choice Award)</p>

Status: Complete