Bharath Subramanian Pichai
The proliferation of heterogeneous compute platforms, of which CPU/GPU is a prevalent example, necessitates a manageable programming model to ensure widespread adoption. A key component of this is a shared unified address space between the heterogeneous units to obtain the programmability benefits of virtual memory. Indeed, processor vendors have already begun embracing heterogeneous systems with […]
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R. Mokhtari, M. Stumm
GPUs offer an order of magnitude higher compute power and memory bandwidth than CPUs. GPUs therefore might appear to be well suited to accelerate computations that operate on voluminous data sets in independent ways; e.g., for transformations, filtering, aggregation, partitioning or other ”Big Data” style processing. Yet experience indicates that it is difficult, and often […]
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Akihiko Kasagi, Koji Nakano, and Yasuaki Ito
The Hierarchical Memory Machine (HMM) is a theoretical parallel computing model that captures the essence of computation on CUDA-enabled GPUs. The offline permutation is a task to copy numbers stored in an array a of size n to an array b of the same size along a permutation P given in advance. A conventional algorithm […]
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Minsoo Rhu, Michael Sullivan, Jingwen Leng, Mattan Erez
As GPU’s compute capabilities grow, their memory hierarchy increasingly becomes a bottleneck. Current GPU memory hierarchies use coarse-grained memory accesses to exploit spatial locality, maximize peak bandwidth, simplify control, and reduce cache meta-data storage. These coarse-grained memory accesses, however, are a poor match for emerging GPU applications with irregular control flow and memory access patterns. […]
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Thomas M. Baumann, Jose Gracia
Valgrind, and specifically the included tool Memcheck, offers an easy and reliable way for checking the correctness of memory operations in programs. This works in an unintrusive way where Valgrind translates the program into intermediate code and executes it on an emulated CPU. The heavy weight tool Memcheck uses this to keep a full shadow […]
Alexandru Pirjan
In this paper, there are depicted optimization solutions for the segmented sum algorithmic function, developed using the Compute Unified Device Architecture (CUDA), a powerful and efficient solution for optimizing a wide range of applications. The parallel-segmented sum is often used in building many data processing algorithms and through its optimization, one can improve the overall […]
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Leonid Djinevski, Sime Arsenovski, Sasko Ristov, Marjan Gusev
GPU devices offer great performance when dealing with algorithms that require intense computational resources. A developer can configure the L1 cache memory of the latest GPU Kepler architecture with different cache size and cache set associativity, per Streaming Multiprocessors (SM). The performance of the computation intensive algorithms can be affected by these cache parameters. In […]
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C.P.Patidar, Meena Sharma
In this paper we implement histogram computations on a Graphics Processing Unit (GPU). Our Histogram computations is implemented using compute unified device architecture (CUDA) which is a minimal extension to C/C++. In this development Histogram computations, computed on GPU’s global memory as well as on shared memory. We also perform Histogram computations on CPU and […]
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S. Kaxiras, G. Keramidas, K. Koukos
In this work we are (a) exploring memory slack for the state-of-the-art many-core CPUs and GPUs, (b) present techniques to eliminate slack, and (c) explore the architectural parameters to improve power eciency. Dynamic Voltage-Frequency Scaling (DVFS) is one of the most bene cial techniques for CPU’s to improve power eciency. The end of Dennard scaling however, […]
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C. Sangani, M. Venkatesan, R. Ramesh
Computer architecture is at the brink of convergence with the integration of the general-purpose multi-core CPU architecture and the special purpose accelerated graphics architecture (GPU). Semiconductor giants like Intel and AMD have already brought to the market next-generation integrated heterogeneous processors in the form of the Sandy Bridge and the Fusion architecture respectively. However, with […]
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Alamelu Sankaranarayanan, Ehsan K. Ardestani, Jose Luis Briz, Jose Renau
With progressive generations and the ever-increasing promise of computing power, GPGPUs have been quickly growing in size, and at the same time, energy consumption has become a major bottleneck for them. The first level data cache and the scratchpad memory are critical to the performance of a GPGPU, but they are extremely energy inefficient due […]
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Ahmad Lashgar, Amirali Baniasadi, Ahmad Khonsari
There are a number of design decisions that impact a GPU’s performance. Among such decisions deciding the right warp size can deeply influence the rest of the design. Small warps reduce the performance penalty associated with branch divergence at the expense of a reduction in memory coalescing. Large warps enhance memory coalescing significantly but also […]
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Free GPU computing nodes at

Registered users can now run their OpenCL application at 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 11.4
  • SDK: AMD APP SDK 2.8
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 5.0.35, AMD APP SDK 2.8

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