ArrayFire v2.0 is now available for download. The second iteration of our free, fast, and simple GPU library now supports both CUDA and OpenCL devices. Major Updates ArrayFire now works on OpenCL enabled devices New and improved documentation Optimized for new GPUs–NVIDIA Kepler (K20) and AMD Tahiti (7970) New in ArrayFire OpenCL Same APIs as ArrayFire CUDA version Supports both Linux and Windows Just In Time Compilation (JIT) of kernels Parallel for: gfor Accelerated algorithms in the following domains Image Processing Signal Processing Data Analysis and Statistics Visualization And more New in ArrayFire CUDA New Signal and Image processing functions Faster transpose and matrix multiplication Better debugging support for GDB and Visual Studio Bug fixes to make overall experience better For a more complete list of the …
Application Time vs Solver Time
Last week, HPCwire ran an interesting article entitled, “Where has HPC’s math gone?” The article analyzes the increasing importance of math solvers to successful HPC outcomes. As the number of cores grows, the percentage of time HPC codes spend in solvers increases significantly. The following chart illustrates this trend nicely: ArrayFire is ideally suited for HPC applications that need to accelerate the toughest math problems. ArrayFire contains hundreds of math functions across numerous domains. In general, if the HPC community really wants to solve this problem, it will begin to invest more in libraries than in compilers that have no chance at optimizing these tough math problems automatically. Rather, it is only through expertly-tuned codes, such as those developed …
ArrayFire Examples (Part 7 of 8) – PDE
This is the seventh in a series of posts looking at our current ArrayFire examples. The code can be compiled and run from arrayfire/examples/ when you download and install the ArrayFire library. Today we will discuss the examples found in the pde/ directory. In these examples, my machine has the following configuration: ArrayFire v1.9.1 (build XXXXXXX) by AccelerEyes (64-bit Linux) License: XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX CUDA toolkit 5.0, driver 319.17 GPU0 Tesla K20c, 5120 MB, Compute 3.5 (current) GPU1 Tesla C2075, 6144 MB, Compute 2.0 GPU2 Tesla C1060, 4096 MB, Compute 1.3 Display Device: GPU0 Tesla K20c Memory Usage: 5044 MB free (5120 MB total) The followings are the examples of formulating Partial Differential Equations, generally used to create a relevant computer model with several variables. In these examples, …
ArrayFire Examples (Part 6 of 8) – Multiple GPUs
This is the sixth in a series of posts looking at our current ArrayFire examples. The code can be compiled and run from arrayfire/examples/ when you download and install the ArrayFire library. Today we will discuss the examples found in the multi_gpu/ directory. In these examples, my machine has the following configuration: ArrayFire v1.9.1 (build XXXXXXX) by AccelerEyes (64-bit Linux) License: XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX CUDA toolkit 5.0, driver 319.17 GPU0 Tesla K20c, 5120 MB, Compute 3.5 (current) GPU1 Tesla C2075, 6144 MB, Compute 2.0 GPU2 Tesla C1060, 4096 MB, Compute 1.3 Memory Usage: 4935 MB free (5120 MB total) *The following order represents the speed of GPUs in my machine from fastest to slowest: K20c, C2070, C1060. ArrayFire is capable of multi-GPU management. This capability becomes useful for benchmarking …
ArrayFire Examples (Part 5 of 8) – Machine Learning
This is the fifth in a series of posts looking at our current ArrayFire examples. The code can be compiled and run from arrayfire/examples/ when you download and install the ArrayFire library. Today we will discuss the examples found in the machine_learning/ directory. In these examples, my machine has the following configuration: ArrayFire v1.9 (build XXXXXXX) by AccelerEyes (64-bit Mac OSX) License: XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX CUDA toolkit 5.0, driver 304.54 GPU0 GeForce GT 560M, 1024 MB, Compute 3.0 (single,double) Display Device: GPU0 GeForce GT 650M Memory Usage: 245 MB free (1024 MB total)… 1. K-Means Clustering – kmeans.cpp Figure 1 This is an example of K-Means Clustering Algorithm. K-Means Clustering Algorithm is a data mining technique that partitions the given data into groups by their similarities. All you need to …
Solution to NVIDIA Toolkit Installation Error for Ubuntu 12.10 [Driver: Installation Failed]
Driver: Installation Failed You may find this error message while trying to set up the NVIDIA CUDA Toolkit in Ubuntu. I found it when I was installing the toolkit for ArrayFire [1] CUDA Toolkit Installation 1. Download the CUDA Toolkit in the link. 2. Extract the .run file in a location sudo sh cuda_5.0.35_linux_64_ubuntu11.10-1.run –extract <location> 3. Exit the X server (press Ctrl+Alt+F1) and stop the display manager by the following command. sudo stop lightdm 4. cd to the location and now there are run files named samples*, devdriver* and cudatoolkit*. 5. Install devdriver (*only if NVIDIA Driver is not installed) sudo sh devdriver_5.0_linux_64_304.54.run 6. Install cudatoolkit sudo sh cudatoolkit-5.0.35_linux_64_ubuntu11.10.run In the end, when it asks …
Beamforming with ArrayFire
Alessandro Savoia and researchers at Università degli Studi Roma Tre have achieved an order of magnitude improvement in the performance of a beamforming application using ArrayFire for GPU acceleration with CUDA-capable NVIDIA GPUs. This application involves conventional beamforming. Steps include the application of a time delay to each signal vector, summation across all vectors, and processing on the result. Processing includes demodulation, envelope extraction, and logarithmic compression. ArrayFire’s functions for shifting, interpolation, and filtering made this application possible for acceleration on GPUs and reduced the time to develop significantly. Alessandro’s benchmarks show that a CPU-only version was only running at 1 frame/sec, while the ArrayFire-accelerated version was running at 10-20 frames/sec, depending on the dataset. Alessandro and his team are looking forward to …
ArrayFire Examples (Part 4 of 8) – Image Processing
This is the fourth in a series of posts looking at our current ArrayFire examples. The code can be compiled and run from arrayfire/examples/ when you download and install the ArrayFire library. Today we will discuss the examples found in the image_processing/ directory. In these examples, my machine has the following configuration: ArrayFire v1.9 (build XXXXXXX) by AccelerEyes (64-bit Windows) License: XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX CUDA toolkit 5.0, driver 306.94 GPU0 GeForce GT 650M, 2048 MB, Compute 3.0 (single,double) Display Device: GPU0 GeForce GT 650M Memory Usage: 1981 MB free (2048 MB total)… Image Demo The purpose of this example is to show how to do some common image manipulations. The method channel_split shows how easily multi-dimensional arrays can be subdivided: // Split a MxNx3 image into 3 separate channel …
ArrayFire + Scorpii Demo by CreativeC
CreativeC makes awesome compute + visualization systems. We got to see the demo in live action at the GPU Technology Conference last month. Tim Thomas was kind enough to let us film the demo showing how ArrayFire can be used to drive a multi-node, 9 GPU system in a physics application. Checkout the video below. If you are interested in high-throughput compute coupled with high-pixel visualizations, we recommend you talk with the folks at CreativeC. They are always pushing the envelope on what can be done with GPU computing and GPU visualizations. Also, if you have cool demos showing ArrayFire in action, let us know. We’d love to film your work and make it available on this blog! Related articles …
ArrayFire Examples (Part 3 of 8) – Financial
This is the third in a series of posts looking at our current ArrayFire examples. The code can be compiled and run from arrayfire/examples/ when you download and install the ArrayFire library. Today we will discuss the examples found in the financial/ directory. In these examples, my machine has the following configuration: ArrayFire v1.9 (build XXXXXXX) by AccelerEyes (64-bit Linux) License: XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX CUDA toolkit 5.0, driver 304.54 GPU0 Quadro 6000, 6144 MB, Compute 2.0 (single,double) Display Device: GPU0 Quadro 6000 Memory Usage: 5549 MB free (6144 MB total)… Black-Scholes There are a number of applications of ArrayFire and GPU programming in the world of finance and markets. Here we have an example of Black-Scholes, which is a model for computing options prices in the stock market. Understanding …