Aug, 27

gScan: Accelerating Graham Scan on the GPU

This paper presents a fast implementation of the Graham scan on the GPU. The proposed algorithm is composed of two stages: (1) two rounds of preprocessing performed on the GPU and (2) the finalization of finding the convex hull on the CPU. We first discard the interior points that locate inside a quadrilateral formed by […]
Aug, 27

Adaptive Multi-GPU Exchange Monte Carlo for the 3D Random Field Ising Model

The study of disordered spin systems through Monte Carlo simulations has proven to be a hard task due to the adverse energy landscape present at the low temperature regime, making it difficult for the simulation to escape from a local minimum. Replica based algorithms such as the Exchange Monte Carlo (also known as parallel tempering) […]
Aug, 27

Accelerated Deep Learning using Intel Xeon Phi

Deep learning, a sub-topic of machine learning inspired by biology, have achieved wide attention in the industry and research community recently. State-of-the-art applications in the area of computer vision and speech recognition (among others) are built using deep learning algorithms. In contrast to traditional algorithms, where the developer fully instructs the application what to do, […]
Aug, 27

CudaChain: A Practical GPU-accelerated 2D Convex Hull Algorithm

This paper presents a practical GPU-accelerated convex hull algorithm and a novel Sorting-based Preprocessing Approach (SPA) for planar point sets. The proposed algorithm consists of two stages: (1) two rounds of preprocessing performed on the GPU and (2) the finalization of calculating the expected convex hull on the CPU. We first discard the interior points […]
Aug, 27

MemcachedGPU: Scaling-up Scale-out Key-value Stores

This paper tackles the challenges of obtaining more efficient data center computing while maintaining low latency, low cost, programmability, and the potential for workload consolidation. We introduce GNoM, a software framework enabling energy-efficient, latency bandwidth optimized UDP network and application processing on GPUs. GNoM handles the data movement and task management to facilitate the development […]
Aug, 24

First International Workshop on Heterogeneous High-performance Reconfigurable Computing (H2RC’15), 2015

With Exascale systems on the horizon at the same time that conventional von-Neumann architectures are suffering from rising power densities, we are facing an era with power, energy-efficiency, and cooling as first-class constraints for scalable HPC. FPGAs can tailor the hardware to the application, avoiding overheads of general-purpose architectures–for example, through customized datapaths and memory […]
Aug, 24

Performance Evaluations of Document-Oriented Databases using GPU and Cache Structure

Document-oriented databases are popular databases, in which users can store their documents in a schema-less manner and perform search queries for them. They have been widely used for web applications that process a large collection of documents because of their high scalability and rich functions. One of major functions of documentoriented databases is a string […]
Aug, 24

Viability of Feature Detection on Sony Xperia Z3 using OpenCL

CONTEXT: Embedded platforms GPUs are reaching a level of performance comparable to desktop hardware. Therefore it becomes interesting to apply Computer Vision techniques to modern smartphones.The platform holds different challenges, as energy use and heat generation can be an issue depending on load distribution on the device. OBJECTIVES: We evaluate the viability of a feature […]
Aug, 24

Scheduling for new computing platforms with GPUs

More and more computers use hybrid architectures combining multi-core processors (CPUs) and hardware accelerators like GPUs (Graphics Processing Units). These hybrid parallel platforms require new scheduling strategies. This work is devoted to a characterization of this new type of scheduling problems. The most studied objective in this work is the minimization of the makespan, which […]
Aug, 24

Source-to-Source Automatic Program Transformations for GPU-like Hardware Accelerators

Since the beginning of the 2000s, the raw performance of processors stopped its exponential increase. The modern graphic processing units (GPUs) have been designed as array of hundreds or thousands of compute units. The GPUs’ compute capacity quickly leads them to be diverted from their original target to be used as accelerators for general purpose […]
Aug, 24

Semi-Global Filtering of Airborne LiDAR Data for Fast Extraction of Digital Terrain Models

Automatic extraction of ground points, called filtering, is an essential step in producing Digital Terrain Models from airborne LiDAR data. Scene complexity and computational performance are two major problems that should be addressed in filtering, especially when processing large point cloud data with diverse scenes. This paper proposes a fast and intelligent algorithm called Semi-Global […]
Aug, 21

A CPU and GPU Heterogeneous Processing of Multimedia Data by using OpenCL

In recent times, it has become possible to parallelize many multimedia applications using multicore platforms such as CPUs and GPUs. In this paper, we propose a parallel processing approach for a multimedia application by using both the CPU and GPU. Instead of distributing the parallelizable workload to either the CPU or GPU, we distribute the […]
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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: nVidia CUDA Toolkit 6.5.14, AMD APP SDK 3.0
Node 2
  • GPU device 0: AMD/ATI Radeon HD 7970 3GB, 1000MHz
  • GPU device 1: AMD/ATI Radeon HD 5870 2GB, 850MHz
  • CPU: Intel Core i7-2600 @ 3.4GHz
  • RAM: 16GB
  • OS: OpenSUSE 12.3
  • SDK: AMD APP SDK 3.0

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