Kuan-Chieh Hsu
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Previous Organizations / Institutes
The organizations / institutes from the scholar's other articles in the past ten years
Research Subjects
Kuan-Chieh Hsu has a total of 26 co-authors. Here are the top co-authors by number of shared publications:
- Adolfy Hoisie
Brookhaven National Laboratory, Upton, NY, USA
3 shared publications - Alexei Klimentov
Brookhaven National Laboratory
3 shared publications - David Keetae Park
Brookhaven National Laboratory, Woodbury, USA
3 shared publications - Joseph Boudreau
University of Pittsburgh
3 shared publications - Norbert Podhorszki
Oak Ridge National Laboratory, Oak Ridge, TN, USA
3 shared publications - Ozgur Ozan Kilic
Brookhaven National Laboratory, Upton, NY, USA
3 shared publications
Latest Publications
- Open Access
- 15 November 2025
- Published by Association for Computing Machinery (ACM)
- p. 968-976
Error Analysis of Globally Distributed Workflow Management System
- 15 November 2025
- Published by Association for Computing Machinery (ACM)
- p. 968-976
Open AccessError Analysis of Globally Distributed Workflow Management System
- Open Access
- 15 November 2025
- Published by Association for Computing Machinery (ACM)
- p. 279-289
Data Management System Analysis for Distributed Computing Workloads
- 15 November 2025
- Published by Association for Computing Machinery (ACM)
- p. 279-289
Open AccessData Management System Analysis for Distributed Computing Workloads
- Open Access
- 15 November 2025
- Published by Association for Computing Machinery (ACM)
- p. 1478-1483
CGSim: A Simulation Framework for Large Scale Distributed Computing Environment
- 15 November 2025
- Published by Association for Computing Machinery (ACM)
- p. 1478-1483
Open AccessCGSim: A Simulation Framework for Large Scale Distributed Computing Environment
- 8 July 2024
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Micro
- Vol. 44 (4) , 11-19
Simultaneous and Heterogenous Multithreading: Exploiting Simultaneous and Heterogeneous Parallelism in Accelerator-Rich Architectures
The addition of domain-specific hardware accelerators and general-purpose processors supporting vector and scalar models makes modern computers undoubtedly heterogeneous. However, existing programming models and runtime systems target using the most efficient category of processing units to delegate computation from each code region, undermining the potential parallelism that heterogeneous processing units can provide. Simultaneous and heterogenous multithreading (SHMT) is a programming and execution model that activates all possible heterogeneous processing units for computation from a code region to enable “real” heterogeneous parallelism. SHMT presents an abstraction and a runtime system to facilitate parallel execution. Despite the new type of parallelism, SHMT also needs to additionally address the heterogeneity in data precision that various processing units support to ensure the quality of the result. This paper implements and evaluates SHMT on an embedded system platform with a GPU and an Edge TPU. SHMT achieves up to 1.95× speedup and 51.0% energy reduction compared to GPU baseline.
- 8 July 2024
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Micro
- Vol. 44 (4) , 11-19
Simultaneous and Heterogenous Multithreading: Exploiting Simultaneous and Heterogeneous Parallelism in Accelerator-Rich Architectures
The addition of domain-specific hardware accelerators and general-purpose processors supporting vector and scalar models makes modern computers undoubtedly heterogeneous. However, existing programming models and runtime systems target using the most efficient category of processing units to delegate computation from each code region, undermining the potential parallelism that heterogeneous processing units can provide. Simultaneous and heterogenous multithreading (SHMT) is a programming and execution model that activates all possible heterogeneous processing units for computation from a code region to enable “real” heterogeneous parallelism. SHMT presents an abstraction and a runtime system to facilitate parallel execution. Despite the new type of parallelism, SHMT also needs to additionally address the heterogeneity in data precision that various processing units support to ensure the quality of the result. This paper implements and evaluates SHMT on an embedded system platform with a GPU and an Edge TPU. SHMT achieves up to 1.95× speedup and 51.0% energy reduction compared to GPU baseline.
- Open Access
- 28 October 2023
- Published by Association for Computing Machinery (ACM)
- p. 137-152
Simultaneous and Heterogenous Multithreading
The landscape of modern computers is undoubtedly heterogeneous, as all computing platforms integrate multiple types of processing units and hardware accelerators. However, the entrenched programming models focus on using only the most efficient processing units for each code region, underutilizing the processing power within heterogeneous computers. This paper simultaneous and heterogenous multithreading (SHMT), a programming and execution model that enables opportunities for “real” parallel processing using heterogeneous processing units. In contrast to conventional models, SHMT can utilize heterogeneous types of processing units concurrently for the same code region. Furthermore, SHMT presents an abstraction and a runtime system to facilitate parallel execution. More importantly, SHMT needs to additionally address the heterogeneity in data precision that various processing units support to ensure the quality of the result. This paper implements and evaluates SHMT on an embedded system platform with a GPU and an Edge TPU. SHMT achieves up to 1.95 × speedup and 51.0% energy reduction compared to GPU baseline.
- 28 October 2023
- Published by Association for Computing Machinery (ACM)
- p. 137-152
Open AccessSimultaneous and Heterogenous Multithreading
The landscape of modern computers is undoubtedly heterogeneous, as all computing platforms integrate multiple types of processing units and hardware accelerators. However, the entrenched programming models focus on using only the most efficient processing units for each code region, underutilizing the processing power within heterogeneous computers. This paper simultaneous and heterogenous multithreading (SHMT), a programming and execution model that enables opportunities for “real” parallel processing using heterogeneous processing units. In contrast to conventional models, SHMT can utilize heterogeneous types of processing units concurrently for the same code region. Furthermore, SHMT presents an abstraction and a runtime system to facilitate parallel execution. More importantly, SHMT needs to additionally address the heterogeneity in data precision that various processing units support to ensure the quality of the result. This paper implements and evaluates SHMT on an embedded system platform with a GPU and an Edge TPU. SHMT achieves up to 1.95 × speedup and 51.0% energy reduction compared to GPU baseline.