
In this paper we introduce, Bohrium, a runtime-system for mapping vector operations onto a number of different hardware platforms, from simple multi-core systems to clusters and GPU enabled systems. In order to make efficient choices Bohrium is implemented as a virtual machine that makes runtime decisions, rather than a statically compiled library, which is the more common approach. In principle, Bohrium can be used for any programming language but for now, the supported languages are limited to Python, C++ and the. Net framework, e.g. C# and F#. The primary success criteria are to maintain a complete abstraction from low-level details and to provide efficient code execution across different, current and future, processors. We evaluate the presented design through a setup that targets a multi-core CPU, an eight-node Cluster, and a GPU, all preliminary prototypes. The evaluation includes three well-known benchmark applications, Black Sholes, Shallow Water, and N-body, implemented in C++, Python, and C# respectively.
High-Productivity, GPU, Heterogeneous Computing, Parallel Programming Environment
High-Productivity, GPU, Heterogeneous Computing, Parallel Programming Environment
| selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 12 | |
| popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Average | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Top 10% | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Top 10% |
