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.

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: In Progress
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: In Progress
Run Markov Chain Monte Carlo (MCMC) in Parallel for Evolutionary Study
Texas Tech University

<p>My ongoing project is focused on using species trait value (as data matrices) and its corresponding phylogenetic relationship (as a distance matrix) to reconstruct the evolutionary history of the smoke-induced seed germination trait. The results of this project are expected to increase the predictability of which untested species could benefit from smoke treatment, which could promote germination success of native species in ecological restoration. This computational resources allocated for this project pull from the high-memory partition of our Ivy cluster of HPCC (Centos 8, Slurm 20.11, 1.5 TB memory/node, 20 core /node, 4 node). However, given that I have over 1300 species to analyze, using the maximum amount of resources to speed up the data analysis is a challenge for two reasons: (1) the ancestral state reconstruction (the evolutionary history of plant traits) needs to use the Markov Chain Monte Carlo (MCMC) in Bayesian statistics, which runs more than 10 million steps and, according to experienced evolutionary biologists, could take a traditional single core simulation up 6 months to run; and (2) my data contain over 1300 native species, with about 500 polymorphic points (phylogenetic uncertainty), which would need a large scale of random simulation to give statistical strength. For instance, if I use 100 simulations for each 500 uncertainty points, I would have 50,000 simulated trees. Based on my previous experience with simulations, I could design codes to parallel analyze 50,000 simulated trees but even with this parallelization the long run MCMC will still require 50000 cores to run for up to 6 months. Given this computational and evolutionary research challenge, my current work is focused on discovering a suitable parallelization methods for the MCMC steps. I hope to have some computational experts to discuss my project.</p>

Status: In Progress