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 Parallelizing Matlab Compiler Framework and Run time for Heterogeneous Systems

Compute-intensive applications incorporate ever increasing data processing requirements on hardware systems. Many of these applications have only recently become feasible thanks to the increasing computing power of modern processors. The Matlab language is uniquely situated to support the description of these compute-intensive scientific applications, and consequently has been continuously improved to provide increasing computational support […]
Aug, 21

Implementing Computer Vision Functions with OpenCL on the Qualcomm Adreno 420

Computer vision algorithms are becoming increasingly important in mobile, embedded, and wearable devices and applications. These compute-intensive workloads are challenging to implement with good performance and power-efficiency. In many applications, implementing critical portions of computer vision workloads on a general-purpose graphics processing unit (GPU) is an attractive solution. Qualcomm enables programming of the Adreno GPU […]
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 […]
Aug, 21

Locality Aware Work-Stealing Based Scheduling in Hybrid CPU-GPU

We study work-stealing based scheduling on a cluster of nodes with CPUs and GPUs. In particular, we evaluate locality aware scheduling in the context of distributed shared memory style programming, where the user is oblivious to data placement. Our runtime maintains a distributed map of data resident on various nodes and uses it to estimate […]
Aug, 21

GPU computing with OpenCL to model 2D elastic wave propagation: exploring memory usage

Graphics processing units (GPUs) have become increasingly powerful in recent years. Programs exploring the advantages of this architecture could achieve large performance gains and this is the aim of new initiatives in high performance computing. The objective of this work is to develop an efficient tool to model 2D elastic wave propagation on parallel computing […]
Aug, 18

OpenCL-Based Design of an FPGA Accelerator for Phase-Based Correspondence Matching

This paper proposes a Field Programmable Gate Array (FPGA) implementation of the stereo correspondence matching using Phase-Only Correlation (POC). The use of high-accuracy stereo correspondence matching based on POC makes it possible to measure accurate 3D shape of an object using stereo vision. The drawback of the POC-based approach is its high computational cost. To […]
Aug, 18

Parallelizing a high-order WENO scheme for complicated flow structures on GPU and MIC

As a conservative, high-order accurate, shock-capturing method, weighted essentially non-oscillatory (WENO) scheme have been widely used to effectively resolve complicated flow structures in computational fluid dynamics (CFD) simulations. However, using a high-order WENO scheme can be highly time-consuming, which greatly limits the CFD application’s performance efficiency. In this paper, we present various parallel strategies base […]
Aug, 18

Runtime Code Generation and Data Management for Heterogeneous Computing in Java

GPUs (Graphics Processing Unit) and other accelerators are nowadays commonly found in desktop machines, mobile devices and even data centres. While these highly parallel processors offer high raw performance, they also dramatically increase program complexity, requiring extra effort from programmers. This results in difficult-to-maintain and non-portable code due to the low-level nature of the languages […]
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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: nVidia GeForce GTX 560 Ti 2GB, 822MHz
  • 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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