Hybrid computing: CPU+GPU co-processing and its application to tomographic reconstruction
Modern computers are equipped with powerful computing engines like multicore processors and GPUs. The 3DEM community has rapidly adapted to this scenario and many software packages now make use of high performance computing techniques to exploit these devices. However, the implementations thus far a...
| Autores: | , , , |
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| Tipo de documento: | artigo |
| Estado: | Versión enviada para evaluación y publicación |
| Data de publicação: | 2012 |
| País: | España |
| Recursos: | Consejo Superior de Investigaciones Científicas (CSIC) |
| Repositório: | DIGITAL.CSIC. Repositorio Institucional del CSIC |
| OAI Identifier: | oai:digital.csic.es:10261/379856 |
| Acesso em linha: | http://hdl.handle.net/10261/379856 |
| Access Level: | Acceso aberto |
| Palavra-chave: | CPU GPU Hybrid computing CPU–GPU co-processing High performance computing Electron tomography Tomographic reconstruction |
| Resumo: | Modern computers are equipped with powerful computing engines like multicore processors and GPUs. The 3DEM community has rapidly adapted to this scenario and many software packages now make use of high performance computing techniques to exploit these devices. However, the implementations thus far are purely focused on either GPUs or CPUs. This work presents a hybrid approach that collaboratively combines the GPUs and CPUs available in a computer and applies it to the problem of tomographic reconstruction. Proper orchestration of workload in such a heterogeneous system is an issue. Here we use an on-demand strategy whereby the computing devices request a new piece of work to do when idle. Our hybrid approach thus takes advantage of the whole computing power available in modern computers and further reduces the processing time. This CPU+GPU co-processing can be readily extended to other image processing tasks in 3DEM. |
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