12089
Li Tian, Fugen Zhou, Cai Meng
We address the problem that multicore DSP system doesn’t support OpenCL programming. We designed compiler and proposed a runtime framework for TI multicore DSP, by which OpenCL parallel program could take advantage of multicore computing resource. Firstly, we make use of the LLVM and Clang compiler front-end to achieve source-to-source translation and in the next […]
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Iype P. Joseph
Multicore CPUs (Central Processing Units) and GPUs (Graphics Processing Units) are omnipresent in today’s market-leading smartphones and tablets. With CPUs and GPUs getting more complex, maximizing hardware utilization is becoming problematic. The challenges faced in GPGPU (General Purpose computing using GPU) computing on embedded platforms are different from their desktop counterparts due to their memory […]
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Arslan Munir, Sanjay Ranka, Ann Gordon-Ross
With Moore’s law supplying billions of transistors on-chip, embedded systems are undergoing a transition from single-core to multicore to exploit this high-transistor density for high performance. Embedded systems differ from traditional high-performance supercomputers in that power is a first-order constraint for embedded systems; whereas, performance is the major benchmark for supercomputers. The increase in on-chip […]
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Siddharth Nilakantan, Srikanth Annangi, Nikhil Gulati, Karthik Sangaiah, Mark Hempstead
Increasing chip power density has brought application specific accelerator architectures to the forefront as an energy and area efficient solution. While GPGPU systems take advantage of specialized hardware to perform computationally intensive tasks faster than chip multiprocessor (CMP) systems, accelerators are hardware units that are designed to execute a specific application efficiently. Real-time ultrasound imaging […]
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Kulin V. Seth
The technology community is rapidly moving away from the age of computers and laptops, and is entering the emerging era of hand-held devices. With the rapid development of smart phones, tablets, and pads, there has been widespread adoption of Graphic Processing Units (GPUs) in the embedded space. The hand-held market is now seeing an ever […]
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Jason Loew, Jesse Elwell, Dmitry Ponomarev, Patrick H. Madden
Embedded systems are designed to perform a specific set of tasks, and are frequently found in mobile, power-constrained environments. There is growing interest in the use of parallel computation as a means to increase performance while reducing power consumption. In this paper, we highlight fundamental limits to what can and cannot be improved by parallel […]
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Shuai Mu, Chenxi Wang, Ming Liu, Dongdong Li, Maohua Zhu, Xiaoliang Chen, Xiang Xie, Yangdong Deng
Today’s high performance embedded computing applications are posing significant challenges for processing throughout. Traditionally, such applications have been realized on application specific integrated circuits (ASICs) and/or digital signal processors (DSP). However, ASICs’ advantage in performance and power often could not justify the fast increasing fabrication cost, while current DSP offers a limited processing throughput that […]
Muhsen Owaida, Nikolaos Bellas, Konstantis Daloukas, Christos D. Antonopoulos
The problem of automatically generating hardware modules from a high level representation of an application has been at the research forefront in the last few years. In this paper, we use OpenCL, an industry supported standard for writing programs that execute on multicore platforms and accelerators such as GPUs. Our architectural synthesis tool, SOpenCL (Silicon-OpenCL), […]
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T. Scogland, H. Lin, W. Feng
The graphics processing unit (GPU) has evolved from a single-purpose graphics accelerator to a tool that can greatly accelerate the performance of high-performance computing (HPC) applications. Previous studies have shown that discrete GPUs, while energy efficient for compute-intensive scientific applications, consume very high power. In fact, a compute-capable discrete GPU can draw more than 200 […]

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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
  • GPU device 0: AMD/ATI Radeon HD 5870 2GB, 850MHz
  • 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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