Workload Placement on Heterogeneous CPU-GPU Systems
Saved in:
| Title: | Workload Placement on Heterogeneous CPU-GPU Systems |
|---|---|
| Authors: | Nogueira Lobo de Carvalho, Marcos, Simitsis, Alkis, Queralt Calafat, Anna, Romero Moral, Óscar |
| Source: | UPCommons. Portal del coneixement obert de la UPC Universitat Politècnica de Catalunya (UPC) |
| Publisher Information: | Association for Computing Machinery (ACM), 2024. |
| Publication Year: | 2024 |
| Subject Terms: | Graphics processing unit, Àrees temàtiques de la UPC::Informàtica::Enginyeria del software, Data reduction, Àrees temàtiques de la UPC::Informàtica::Arquitectura de computadors, Digital storage, Computer graphics equipment |
| Description: | The popularity of heterogeneous CPU-GPU processing has increased considerably in recent years. To efficiently utilize heterogeneous resources, data processing systems depend on an appropriate workload placement strategy to assign the right amount of compute to the right processor. However, finding an optimal placement strategy is not trivial due to various complex and conflicting tradeoffs related to the characteristics of processors, the nature of the workload, and data locality. In addition, placement decisions impact workload runtime and performance cost, and also depend on the availability of potentially different implementations for CPUs and GPUs, which adds extra complexity in such heterogeneous environments. In this tutorial, we review and compare state-of-the-art strategies for workload placement on heterogeneous CPU-GPU architectures, along with runtime prediction techniques and methods to support multi-device code. We also discuss open issues and identify potentially promising future research directions. |
| Document Type: | Article |
| File Description: | application/pdf |
| Language: | English |
| ISSN: | 2150-8097 |
| DOI: | 10.14778/3685800.3685845 |
| Access URL: | https://hdl.handle.net/2117/426961 https://doi.org/10.14778/3685800.3685845 |
| Rights: | CC BY NC ND |
| Accession Number: | edsair.doi.dedup.....fb3e05725255cb8a35a9ef93d09f06f2 |
| Database: | OpenAIRE |
Full Text Finder 
Nájsť tento článok vo Web of Science | Abstract: | The popularity of heterogeneous CPU-GPU processing has increased considerably in recent years. To efficiently utilize heterogeneous resources, data processing systems depend on an appropriate workload placement strategy to assign the right amount of compute to the right processor. However, finding an optimal placement strategy is not trivial due to various complex and conflicting tradeoffs related to the characteristics of processors, the nature of the workload, and data locality. In addition, placement decisions impact workload runtime and performance cost, and also depend on the availability of potentially different implementations for CPUs and GPUs, which adds extra complexity in such heterogeneous environments. In this tutorial, we review and compare state-of-the-art strategies for workload placement on heterogeneous CPU-GPU architectures, along with runtime prediction techniques and methods to support multi-device code. We also discuss open issues and identify potentially promising future research directions. |
|---|---|
| ISSN: | 21508097 |
| DOI: | 10.14778/3685800.3685845 |