Dongfang Zhao, Kent Burlingame, Corentin Debains, Pedro Alvarez-Tabio, Ioan Raicu
Reliability is one of the most fundamental challenges for high performance computing (HPC) and cloud computing. Data replication is the de facto mechanism to achieve high reliability, even though it has been criticized for its high cost and low efficiency. Recent research showed promising results by switching the traditional data replication to a software-based RAID. […]
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Ronald Veldema, Michael Philippsen
High-throughput memory management techniques such as malloc/free or mark-and-sweep collectors often exhibit memory fragmentation leaving allocated objects interspersed with free memory holes. Memory defragmentation removes such holes by moving objects around in memory so that they become adjacent (compaction) and holes can be merged (coalesced) to form larger holes. However, known defragmentation techniques are slow. […]
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Aleksandr Khasymski, M. Mustafa Rafique, Ali R. Butt, Sudharshan S. Vazhkudai, Dimitrios S. Nikolopoulos
The exponential growth in user and application data entails new means for providing fault tolerance and protection against data loss. High Performance Computing (HPC) storage systems, which are at the forefront of handling the data deluge, typically employ hardware RAID at the backend. However, such solutions are costly, do not ensure end-to-end data integrity, and […]
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Chulmin Kim, Ki-Woong Park, Kyu Ho Park
Data deduplication has been an effective way to eliminate redundant data mainly for backup storage systems. Since the recent primary storage systems in cloud services are expected to have the redundancy, the deduplication technique can also bring significant cost saving for the primary storage. However, the primary storage system requires high performance requirement about several […]
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Weibin Sun, Robert Ricci, Matthew L. Curry
Many storage systems include computationally expensive components. Examples include encryption for confidentiality, checksums for integrity, and error correcting codes for reliability. As storage systems become larger, faster, and serve more clients, the demands placed on their computational components increase and they can become performance bottlenecks. Many of these computational tasks are inherently parallel: they can […]
Pramod Bhatotia, Rodrigo Rodrigues, Akshat Verma
Redundancy elimination using data deduplication and incremental data processing has emerged as an important technique to minimize storage and computation requirements in data center computing. In this paper, we present the design, implementation and evaluation of Shredder, a high performance content-based chunking framework for supporting incremental storage and computation systems. Shredder exploits the massively parallel […]
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Samer Al-Kiswany, Abdullah Gharaibeh, Matei Ripeanu
Massively multicore processors, such as Graphics Processing Units (GPUs), provide, at a comparable price, a one order of magnitude higher peak performance than traditional CPUs. This drop in the cost of computation, as any order-of-magnitude drop in the cost per unit of performance for a class of system components, triggers the opportunity to redesign systems […]
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Ke Liu, Jingli Zhou, Leihua Qin, Ning Lv
Cloud storage has been increasing in popularity recently due to its ability to deliver virtualized storage on demand over a network. As the amount of digital resources continues to grow at an astounding rate, more and more intelligent devices (such as GPU) are embedded as computing units to enhance the performance of storage system. How […]
K.A. Hawick and D.P. Playne
Many simulations in the physical sciences are expressed in terms of rectilinear arrays of variables. It is attractive to develop such simulations for use in 1-, 2-, 3- or arbitrary physical dimensions and also in a manner that supports exploitation of data-parallelism on fast modern processing devices. We report on data layouts and transformation algorithms […]
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George Lentaris, Dionysios Reisis
Real-time graphics applications require memory organizations featuring parallel pixel access and low-cost implementation. This work bases on a nonlinear skew mapping scheme and exploits the correlation between consecutive requests for pixels to design an efficient parallel memory organization. The mapping achieves parallel access, of mn pixels in various shapes, to the memory organized with mn […]
Samer Al-Kiswany, Abdullah Gharaibeh, Elizeu Santos-Neto, George Yuan, Matei Ripeanu
Today Graphics Processing Units (GPUs) are a largely underexploited resource on existing desktops and a possible cost-effective enhancement to high-performance systems. To date, most applications that exploit GPUs are specialized scientific applications. Little attention has been paid to harnessing these highly-parallel devices to support more generic functionality at the operating system or middleware level. This […]
Abdullah Gharaibeh, Samer A. Kiswany, Sathish Gopalakrishnan, Matei Ripeanu
Massively multicore processors, like, for example, Graphics Processing Units (GPUs), provide, at a comparable price, a one order of magnitude higher peak performance than traditional CPUs. This drop in the cost of computation, as any order-of-magnitude drop in the cost per unit of performance for a class of system components, triggers the opportunity to redesign […]
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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

Completed OpenCL project should be uploaded via User dashboard (see instructions and example there), compilation and execution terminal output logs will be provided to the user.

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