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 …
Giddy for GTC – We’re Taking it to the Next Level
GTC is quickly approaching and AccelerEyes is giddy with excitement! This year we are taking things to the next level as a Silver Sponsor at GTC 2013. Meaning, you’ll be seeing a lot more of us throughout the conference! Schedule a Meeting with Us Do you want to meet with us personally? Schedule a time to sit down with AccelerEyes engineers and account representatives using our online scheduler. Visit our Booth If you’re attending GTC, be sure to come visit us at booth #204 to see some great demos or to chat with anyone in our Software Shop for CUDA & OpenCL. Come see how ArrayFire complements other GPU development efforts, including raw CUDA/OpenCL development, OpenACC, and other GPU libraries. Register …
ArrayFire Examples (Part 2 of 8) – Benchmarks
This is the second 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 benchmarks/ 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)… Blas This example shows a simple bench-marking process using ArrayFire’s matrix multiply routine. For more information on Blas, click here. The data measured in this example is the Giga-Flop (GFLOP Floating Point Operations Per Second). I got the following results using …
ArrayFire Examples (Part 1 of 8) – Getting Started
This is the first 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 getting_started/ directory. Hello World Of course we start with the classic “Hello World” example, which walks you through the basics of using the ArrayFire library. Running this example will print out system and device information, as well as perform some basic matrix operations. This is a good place to get familiar with the basic data container for ArrayFire – the array. ArrayFire v1.9 (build XXXXXXX) by AccelerEyes (64-bit Linux) License: XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX CUDA toolkit 5.0, driver 304.54 GPU0 Quadro 6000, 6144 …
7 Tips for CUDA & OpenCL Programming and How ArrayFire Helps
In order to get the best performance from your CUDA or OpenCL code, it is helpful to keep in mind some useful tips for optimizing performance. Note: By “accelerator” we refer to GPUs, APUs, co-processors, FPGAs, and any devices capable of running CUDA or OpenCL. Vectorized Code: Accelerators perform best with vectorized code because the computations map naturally onto arithmetic cores of the hardware. ArrayFire functions are inherently vectorized, so if you are using ArrayFire, you are writing vectorized code. Memory Transfers: Avoid excessive memory transfers. Each casting operation to and from the accelerator moves data back and forth between CPU memory and accelerator memory. ArrayFire makes many automatic optimizations to minimize these memory transfers by only transferring data when …
ArrayFire Reception in France
As an engineers company, we spend a lot of time wrestling in the weeds of low-level GPU and accelerator codes. This is our battleground, and it can often be dizzying in its complexity. Our whole purpose is to hide that mess and tame those low-level beasts so that ArrayFire users get better performance than anyone else. The joy of ArrayFire comes when we get feedback from ArrayFire users, often from different parts of the world. For instance, the week I share excerpts from two recent emails we received in France: 1) From Barep, a French manufacturing company: “I think ArrayFire is a ‘must have’ library. It’s very easy to use and can be used under Linux and Windows. Personally, I’m happy …