Already the fourth PDF-Monday. It takes quite some time, so I might keep it to 10 in the future – but till then enjoy! Not sure which to read? Pick the first one (for the rest there is not order).

Edit: and the last one, follow me on twitter to see the  PDFs I’m reading. Reason is that hardly anyone clicked on the links to the PDFs.

I would like if you let others know in the comments which PDF you liked a lot.

Adding Physics to Animated Characters with Oriented Particles (Matthias Müller and Nuttapong Chentanez). Discusses how to accelerate movements of pieces of cloth attached to the bodies. Not time to read? There are nice pictures.

John F. Peddy’s analysis on the GPU market.

Hardware/Software Co-Design. Simple Solution to the Matrix Multiplication Problem using CUDA.

CUDA Based Algorithms for Simulating Cardiac Excitation Waves in a Rabbit Ventricle. Bioinformatics.

Real-time implementation of Bayesian models for multimodal perception using CUDA.

GPU performance prediction using parametrized models (Master-thesis by Andreas Resios)

A Parallel Ray Tracing Architecture Suitable for Application-Specific Hardware and GPGPU Implementations (Alexandre S. Nery, Nadia Nedjah, Felipe M.G. Franca, Lech Jozwiak)

Rapid Geocoding of Satellite SAR Images with Refined RPC Model. An ESA-presentation by Lu Zhang, Timo Balz and Mingsheng Liao.

A Parallel Algorithm for Flight Route Planning with CUDA (Master-thesis by Seçkîn Sanci). About the travelling salesman problem and much more.

Color-based High-Speed Recognition of Prints on Extruded Materials. Product-presentation on how to OCR printed text on cables.

Supplementary File of Sequence Homology Search using Fine-Grained Cycle Sharing of Idle GPUs (Fumihiko Ino, Yuma Munekawa, and Kenichi Hagihara). They sped up the BOINC-system (Folding@Home). Bit vague what they want to tell, but maybe you find it interesting.

Parallel Position Weight Matrices Algorithms (Mathieu Giraud, Jean-Stéphane Varré). Bioinformatics, DNA.

GPU-based High Performance Wave Propagation Simulation of Ischemia in Anatomically Detailed Ventricle (Lei Zhang, Changqing Gai, Kuanquan Wang, Weigang Lu, Wangmeng Zuo). Computation in medicine. Ischemia is a restriction in blood supply, generally due to factors in the blood vessels, with resultant damage or dysfunction of tissue

Per-Face Texture Mapping for Realtime Rendering. A Siggraph2011 presentation by Disney and NVidia.

Introduction to Parallel Computing. The CUDA 101 by Victor Eijkhout of University of Texas.

Optimization on the Power Efficiency of GPU and Multicore Processing Element for SIMD Computing. Presentation on what you find out when putting the volt-meter directly on the GPU.

NUDA: Programming Graphics Processors with Extensible Languages. Presentation on NUDA to write less code for GPGPU.

Qt FRAMEWORK: An introduction to a cross platform application and user interface framework. Presentation on the Qt-platform – which has great #OpenCL-support.

Data Assimilation on future computer architectures. The problems projected for 2020.

Current Status of Standards for Augmented Reality (Christine Perey1, Timo Engelke and Carl Reed). not much to do with OpenCL, but tells an interesting purpose for it.

Parallel Computations of Vortex Core Structures in Superconductors (Master-thesis by Niclas E. Wennerdal).

Program the SAME Here and Over There: Data Parallel Programming Models and Intel Many Integrated Core Architecture. Presentation on how to program the Intel MIC.

Large-Scale Chemical Informatics on GPUs (Imran S. Haque, Vijay S. Pande). Book-chapter on the design and optimization of GPU implementations of two popular chemical similarity techniques: Gaussian shape overlay (GSO) and LINGO.

WebGL, WebCL and Beyond! A presentation by Neil Trevett of NVidia/Khronos.

Biomanycores, open-source parallel code for many-core bioinformatics (Mathieu Giraud, Stéphane Janot, Jean-Frédéric Berthelot, Charles Delte, Laetitia Jourdan , Dominique Lavenier , Hélène Touzet, Jean-Stéphane Varré). A short description on the project http://www.biomanycores.org.

As it got more popular that I shared my readings, I decided to put them on my site. I focus on everything that uses vector-processing (GPUs, heterogeneous computing, CUDA, OpenCL, GPGPU, etc). Did I miss something or you have a story you want to share? Contact me or comment on this article. If you tell others about these projects you discovered here, I would appreciate you mention my website or my twitter @StreamHPC.

The research-papers have their authors mentions; the other links can be presentations or overviews of (mostly) products. I have read all, except the long PhD-theses (which are on my non-ad-hoc reading-list) – drop me any question you have.

Bullet Physics, Autodesk style. AMD and Autodesk on integrating Bullet Physics engine into Maya.

MERCUDA: Real-time GPU-based marine scene simulation. OpenCL has enabled more realistic sea and sky simulation for this product, see page 7.

J.P.Morgan: Using Graphic Processing Units (GPUs) in Pricing and Risk. Two pages describing OpenCL/CUDA can give 10 to 100 times speedup over conventional methods.

Parallelization of the Generalized Hough Transform on GPU (Juan Gómez-Luna1a, José María González-Linaresb, José Ignacio Benavidesa, Emilio L. Zapatab and Nicolás Guil). Describing two parallel methods for the Fast Generalized Hough Transform (Fast GHT) using GPUs, implemented in CUDA. It studies how load balancing and occupancy impact the performance of an application on a GPU. Interesting article as it shows that you can choose in which limits you bump into.

Performance Characterization and Optimization of Atomic Operations on AMD GPUs (Marwa Elteir, Heshan Lin and Wu-chun Feng). Measurement of the impact of using atomic operations on AMD GPUs. It seems that even mentioning ‘atomic’ puts the kernel in atomic mode and has major influence on the performance. They also come up with a solution: software-based atomic operation. Work in progress.

On the Efficacy of a Fused CPU+GPU Processor (or APU) for Parallel Computing (Mayank Daga, Ashwin M. Aji, and Wu-chun Feng). Another one from Virginia Tech, this time on AMD’s APUs. This article measures its performance via a set of micro-benchmarks (e.g., PCIe data transfer), kernel benchmarks (e.g., reduction), and actual applications (e.g., molecular dynamics). Very interesting to see in which cases discrete GPUs have a disadvantage even with more muscle power.

A New Approach to rCUDA (José Duato, Antonio J. Peña, Federico Silla1, Juan C. Fernández, Rafael Mayo, and Enrique S. Quintana-Ort). On (remote) execution of CUDA-software within VMs. Interesting if you want powerful machines in your company to delegate heavy work to, or are interested in clouds.

Parallel Smoothers for Matrix-based Multigrid Methods on Unstructured Meshes Using Multicore CPUs and GPUs (Vincent Heuveline, Dimitar Lukarski, Nico Trost and Jan-Philipp Weiss). Different methods around 8 multi-colored Gauß-Seidel type smoothers using OpenMP and GPUs. Also some words on scalability!

Visualization assisted by parallel processing (B. Lange, H. Rey, X. Vasques, W. Puech and N. Rodriguez). How to use GPGPU for visualising big data. An important factor of real-time data-processing is that people get more insight in the matter. As an example they use temperatures in a server-room. As I see more often now, they benchmark CPU, GPU and hybrids.

A New Tool for Classification of Satellite Images Available from Google Maps: Efficient Implementation in Graphics Processing Units (Sergio Bernabéa and Antonio Plaza).  30 times speed-up with a new parallel implementation of the k-means unsupervised clustering algorithm in CUDA. Ity is used for classification of satellite images.

TAU performance System. Product-presentation of TAU which does, among other things, parallel profiling and tracing. Support for CUDA and OpenCL. Extensive collection of tools, so worth to spend time on. An paper released in March describes TAU and compares it with two other performance measurement systems: PAPI and VamirTrace.

An Experimental Approach to Performance Measurement of Heterogeneous Parallel Applications using CUDA (Allen D. Malony, Scott Biersdorff, Wyatt Spear and Shangkar Mayanglambam). Using a TAU-based (see above) tool TAUcuda this paper describes where to focus on when optimising heterogeneous systems.

Speeding up the MATLAB complex networks package using graphic processors (Zhang Bai-Da, Wu Jun-Jie, Tang Yu-Hua and Li Xin). Free registration required. Their conclusion: “In a word, the combination of GPU hardware and MATLAB software with Jacket Toolbox enables high-performance solutions in normal server”. Another PDF I found was: Parallel High Performance Computing with emphasis on Jacket based computing.

Profile-driven Parallelisation of Sequential Programs (Georgios Tournavitis). PhD-thesis on a new approach for extracting and exploiting multiple forms of coarse-grain parallelism from sequential applications written in C.

OpenCL, Heterogeneous Computing, and the CPU. Presentation by Tim Mattson of Intel on how to use OpenCL with the vector-extensions of Intel-processors.

MMU Simulation in Hardware Simulator Based-on State Transition Models (Zhang Xiuping, Yang Guowu and Zheng Desheng). It seems a bit off-chart to have a paper on the Memory Management Unit of a ARM, but as the ARM-processor gets more important some insights on its memory-system is important.

Multi-Cluster Performance Impact on the Multiple-Job Co-Allocation Scheduling (Héctor Blanco, Eloi Gabaldón, Fernando Guirado and Josep Lluí Lérida). This research-group has developed a scheduling-technique, and in this paper they discuss in which situations theirs works better than existing techniques.

Convey Computers: Putting Personality Into High Performance Computing. Product-presentation. They combine X86-CPUs with pre-programmed FPGAs to get high though-put. In short: if you make heavy usage of the provided algorithms, then this might be an alternative to GPGPU.

High-Performance and High-Throughput Computing. What it means for you and your research. Presentation by Philip Chan of Monash University. Though the target-group is their own university, it gives nice insights on how it goes around on other universities and research-groups. HPC is getting cheaper and accepted in more and more types of research.

Bull: Porting seismic software to the GPU. Presentation for oil-companies on finding new oil-fields. These seismic calculations are quite computation-intensive and therefore portable HPC is needed. Know StreamHPC is also assisting in porting such code to GPUs.

Dymaxion: Optimizing Memory Access Patterns for Heterogeneous Systems (Shuai Che, Jeremy W. Sheaffer and Kevin Skadron). This piece of software allows CUDA-programmers to optimize memory mappings to improve the efficiency of memory accesses on heterogeneous platforms.

Real-time volumetric shadows for dynamic rendering (MsC-thesis of Alexandru Teodor V.L. Voicu). Self-shadowing using the Opacity Shadow Maps algorithm is not fit for real-time processing. This thesis discusses Bounding Opacity Maps, a novel method to overcome this problem. Including code at the end, which you can download here.

Accelerating Foreign-Key Joins using Asymmetric Memory Channels (Holger Pirk, Stefan Manegold and Martin Kersten). Shows how to accelerate Foreign-Key Joins by executing the random table lookups on the GPU’s VRAM while sequentially streaming the Foreign-Key-Index through the PCI-E Bus. Very interesting on how to make clever usage of I/O-bounds.

Come back next Monday for more interesting research papers and product presentations. If you have questions, don’t hesitate to contact StreamHPC.