{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/129842"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/129842","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Towards accessible, trustworthy high-performance approximate computing","abstract":"Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2025-10-20 without embargo terms","abstract_html":"Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2025-10-20 without embargo terms","abstract_has_math":false,"creators":["Fink, Zane"],"institution":"University of Illinois Urbana-Champaign","degree_name":"Ph.D.","degree_level":"Dissertation","degree_discipline":"Computer Science","degree_department":null,"school":null,"contributors":["Kale, Laxmikant V.","Olson, Luke","Misailovic, Sasa","Parasyris, Konstantinos"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2025,"date_issued":"2025-07-08","date_published":"2025-07-08","updated_at":"2026-07-22T22:25:05Z","subjects":["High-performance Computing","Gpu","Approximate Computing"],"languages":["en","eng"],"rights":["Copyright 2025 Zane Fink"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/2142/129842","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Kale, Laxmikant V.","Olson, Luke","Misailovic, Sasa","Parasyris, Konstantinos"]},{"key":"dc:creator","label":"Author","values":["Fink, Zane"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2025-07-08","2025-08"]},{"key":"dc:type","label":"Dc Type","values":["text"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Computer Science"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Dissertation"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Ph.D."]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Illinois Urbana-Champaign"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["High-performance Computing","Gpu","Approximate Computing"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en","eng"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2025 Zane Fink"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://hdl.handle.net/2142/129842"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2025-10-20 without embargo terms","The student, Zane Fink, accepted the attached license on 2025-07-07 at 09:52.","The student, Zane Fink, submitted this Dissertation for approval on 2025-07-07 at 10:07.","This Dissertation was approved for publication on 2025-07-08 at 13:23.","DSpace SAF Submission Ingestion Package generated from Vireo submission #22424 on 2025-10-20 at 16:57:29","The deceleration of hardware technology advancements challenges the continued acceleration of scientific simulations that drive discovery. While hardware has evolved toward GPU-dominated heterogeneous architectures, these advances alone cannot meet modern scientific computing requirements. This dissertation explores approximate computing (AC) as a complementary paradigm that trades controlled accuracy loss for substantial performance gains, addressing two fundamental barriers: easy access to AC techniques and trust that approximations meet accuracy requirements. To provide easy access, we develop declarative programming models that abstract implementation complexity while enabling automated exploration of accuracy-performance trade-offs. HPAC-Offload extends OpenMP offload to bring AC techniques to GPU applications, achieving up to 6.9x speedup with less than 10% error by adapting algorithms to GPU architectural constraints. HPAC-ML introduces a directive-based approach for integrating neural network surrogates into scientific applications, automating data collection, model training, and deployment to achieve up to 84x acceleration. While neural network surrogates offer impressive speedups, they generalize poorly to out-of-distribution (OOD) data, potentially producing arbitrarily incorrect results. To increase trust, we systematically evaluate OOD detection techniques through NNUEEHCS, which transforms evaluation from an intractable O(m x n) implementation effort to an O(m+n) plug-and-play workflow. Analysis of over 2,400 models reveals that data-centric approaches like Kernel Density Estimation achieve superior OOD detection on our data suite. Selectively deploying surrogates based on OOD detection creates systematic load imbalance in distributed simulations. To address this, Kombucha, a partial MPI implementation that equips MPI Python programs with Charm4Py load balancing. These components culminate in a blueprint for Trust-Aware Surrogate Systems (TASS) that selectively deploy neural acceleration based on OOD detection. Case studies demonstrate that successful deployment depends on OOD distribution patterns and application structure, with clustered OOD data enabling 3.85x speedup while uniform OOD distribution patterns yield marginal gains. This work establishes practical foundations for approximate computing in scientific applications, providing tools to explore approximation techniques and mechanisms to deploy them safely. By addressing accessibility and trustworthiness, we move toward enabling computational scientists to harness order-of-magnitude performance improvements while maintaining reliability required for scientific discovery in the post-Moore era."]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Towards accessible, trustworthy high-performance approximate computing"]}]}],"canonical_facts":{"dc:contributor":["Kale, Laxmikant V.","Olson, Luke","Misailovic, Sasa","Parasyris, Konstantinos"],"dc:creator":["Fink, Zane"],"dc:date":["2025-07-08","2025-08"],"dc:description":["Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2025-10-20 without embargo terms","The student, Zane Fink, accepted the attached license on 2025-07-07 at 09:52.","The student, Zane Fink, submitted this Dissertation for approval on 2025-07-07 at 10:07.","This Dissertation was approved for publication on 2025-07-08 at 13:23.","DSpace SAF Submission Ingestion Package generated from Vireo submission #22424 on 2025-10-20 at 16:57:29","The deceleration of hardware technology advancements challenges the continued acceleration of scientific simulations that drive discovery. While hardware has evolved toward GPU-dominated heterogeneous architectures, these advances alone cannot meet modern scientific computing requirements. This dissertation explores approximate computing (AC) as a complementary paradigm that trades controlled accuracy loss for substantial performance gains, addressing two fundamental barriers: easy access to AC techniques and trust that approximations meet accuracy requirements. To provide easy access, we develop declarative programming models that abstract implementation complexity while enabling automated exploration of accuracy-performance trade-offs. HPAC-Offload extends OpenMP offload to bring AC techniques to GPU applications, achieving up to 6.9x speedup with less than 10% error by adapting algorithms to GPU architectural constraints. HPAC-ML introduces a directive-based approach for integrating neural network surrogates into scientific applications, automating data collection, model training, and deployment to achieve up to 84x acceleration. While neural network surrogates offer impressive speedups, they generalize poorly to out-of-distribution (OOD) data, potentially producing arbitrarily incorrect results. To increase trust, we systematically evaluate OOD detection techniques through NNUEEHCS, which transforms evaluation from an intractable O(m x n) implementation effort to an O(m+n) plug-and-play workflow. Analysis of over 2,400 models reveals that data-centric approaches like Kernel Density Estimation achieve superior OOD detection on our data suite. Selectively deploying surrogates based on OOD detection creates systematic load imbalance in distributed simulations. To address this, Kombucha, a partial MPI implementation that equips MPI Python programs with Charm4Py load balancing. These components culminate in a blueprint for Trust-Aware Surrogate Systems (TASS) that selectively deploy neural acceleration based on OOD detection. Case studies demonstrate that successful deployment depends on OOD distribution patterns and application structure, with clustered OOD data enabling 3.85x speedup while uniform OOD distribution patterns yield marginal gains. This work establishes practical foundations for approximate computing in scientific applications, providing tools to explore approximation techniques and mechanisms to deploy them safely. By addressing accessibility and trustworthiness, we move toward enabling computational scientists to harness order-of-magnitude performance improvements while maintaining reliability required for scientific discovery in the post-Moore era."],"dc:format":["application/pdf"],"dc:identifier":["https://hdl.handle.net/2142/129842"],"dc:language":["en","eng"],"dc:rights":["Copyright 2025 Zane Fink"],"dc:subject":["High-performance Computing","Gpu","Approximate Computing"],"dc:title":["Towards accessible, trustworthy high-performance approximate computing"],"dc:type":["text"],"thesis:degree_discipline":["Computer Science"],"thesis:degree_level":["Dissertation"],"thesis:degree_name":["Ph.D."],"thesis:institution_name":["University of Illinois Urbana-Champaign"]},"updated_at":"2026-07-22T22:25:05Z"}