8509
Elizabeth A. Thompson, Timothy Anderson
The training phase of the Continuous Space Language Model (CSLM) was implemented in the NVIDIA hardware/software architecture Compute Unified Device Architecture (CUDA). Implementation was accomplished using a combination of CUBLAS library routines and CUDA kernel calls on three different CUDA enabled devices of varying compute capability and a time savings over the traditional CPU approach […]
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Serge Guelton
Heterogeneous computers – platforms that make use of multiple specialized devices to achieve high throughput or low energy consumption – are difficult to program. Hardware vendors usually provide compilers from a C dialect to their machines, but complete application rewriting is frequently required to take advantage of them. In this thesis, we propose a new […]
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Serge Guelton, Francois Irigoin, Ronan Keryell
Hardware accelerators, such as fpga boards or gpu, are an interesting alternative or a valuable complement to classic multi-core processors for computational-intensive software. However it proves to be both costly and difficult to use legacy applications with these new heterogeneous targets. In particular, existing compilers are generally targeted toward code generation for sequential processors and […]

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Free GPU computing nodes at hgpu.org

Registered users can now run their OpenCL application at hgpu.org. We provide 1 minute of computer time per each run on two nodes with two AMD and one nVidia graphics processing units, correspondingly. There are no restrictions on the number of starts.

The platforms are

Node 1
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  • GPU device 1: AMD/ATI Radeon HD 6970 2GB, 880MHz
  • CPU: AMD Phenom II X6 @ 2.8GHz 1055T
  • RAM: 12GB
  • OS: OpenSUSE 13.1
  • SDK: AMD APP SDK 2.9
Node 2
  • GPU device 0: AMD/ATI Radeon HD 7970 3GB, 1000MHz
  • GPU device 1: nVidia GeForce GTX 560 Ti 2GB, 822MHz
  • CPU: Intel Core i7-2600 @ 3.4GHz
  • RAM: 16GB
  • OS: OpenSUSE 12.2
  • SDK: nVidia CUDA Toolkit 6.0.1, AMD APP SDK 2.9

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