Synthetic Aperture Radar on the Jetson TX1

John Melonakos ArrayFire, Case Studies, Computer Vision, CUDA, Image Processing Leave a Comment

Researchers at Peter the Great St.Petersburg Polytechnic University have implemented a synthetic aperture radar processing on the Jetson TX1 Platform using ArrayFire as described in this paper. The paper introduces SAR as "a remote sensing technique producing high-resolution radar images of the Earth’s surface. SAR technology allows obtaining wide swath radar images of objects at a considerable distance regardless of the weather and lighting conditions. It can be used by unmanned aerial vehicles and space satellites. Thus, SAR technology allows solving various problems, such as: detecting small objects (vehicles, airplanes, ships), assessing the state of railways, airfields, seaports, mapping an area, assisting in geological exploration, mapping vegetation, detecting oil spills and pollution as well as many other tasks." The synthetic ...

CUDA Computing on Google Colab with ArrayFire

John Melonakos ArrayFire, Benchmarks, Computing Trends, CUDA, Open Source, Python Leave a Comment

For the first-time in our 14 year existence, we are now able to provide our community with the ability to run ArrayFire programs for free within minutes. Before today, users would have to download and install the library on their own systems, which can be a hassle if you just want to play around with some code and benchmarks. Today, we're excited to announce that ArrayFire is available on Google Colab, the free GPU computing cloud service from Google. Colaboratory, or "Colab" for short, allows you to write and execute Python in your browser, with Zero configuration required, free access to GPUs, and easy sharing. You can jump right in and start playing with this new tool: Click Here to ...

Finger Vein Identity Recognition in "Negligible Time" using ArrayFire

John Melonakos ArrayFire, Case Studies, Computer Vision, CUDA, Open Source Leave a Comment

In this blog post, we summarize work by researchers in Slovakia using ArrayFire to develop OpenFinger, a finger vein identity recognition library. Finger prints and finger veins can be used as a biometric for identity recognition. The physical setup of their sensor system is the following collection of CMOS sensors scattering light to a near infrared LED that projects the image to a CCD camera for capture to a computer. The computing infrastructure used in this work consists of the following components. Several great open source libraries are used, including OpenCV, Caffe, Qt, and ArrayFire. ArrayFire is specifically used in pre-processing to accelerate Gabor filtering on the GPU. Gabor filter has proven itself as one of the most suitable techniques ...

Bringing Together the GPU Computing Ecosystem for Python

John Melonakos Announcements, ArrayFire, Computing Trends, CUDA, Open Source, Python Leave a Comment

To date, we have not done a lot for the Python ecosystem. A few months ago, we decided it was time to change that. Like NVIDIA said in this post, the current slate of GPU tools available to Python developers is scattered. With some attention to community building, perhaps we can build something better -- together. NVIDIA spoke some about its plans to help cleanup the ecosystem. We're onboard with that mentality and have two ways we propose to contribute: We're working on a survey paper that assesses the state of the ecosystem. What technical computing things can you do with each package? What benchmarks result from the packages on real Python user code? What plans does each group have ...

ArrayFire v3.8 Release

John Melonakos Announcements, ArrayFire Leave a Comment

We are excited to share the v3.8 release of ArrayFire! ArrayFire is used in commercial, academic, and government projects around the world, solving some of the toughest computing problems in the most innovative projects. It is well-tested and amazingly fast! In this post, we share some of the major features added to ArrayFire in its 3.8 feature release. The binaries and source code can be downloaded from these locations: Official installers GitHub repository Official APT repository Starting with this release, we will provide Ubuntu packages form our APT repository. To install our packages add our apt repository with the below commands. At this moment we are only supporting bionic(18.04) and focal(20.04). apt-key adv --fetch-key echo "deb [arch=amd64] $(lsb_release ...

The Roaring 20s in AI & Technical Computing

John Melonakos ArrayFire, Computing Trends, Open Source Leave a Comment

Since ArrayFire was founded in 2007, there has been an explosion in software and its importance to our lives. Computers, connected to sensors and real-world outcomes, do really cool things that touch nearly every aspect of our lives. I believe these are exciting times for technical computing and for HPC, as evidenced by the things showcased this week at SC 2020. While ArrayFire focuses purely on software, our hardware partners turn our imaginative lines of code into real-world applications. AMD, NVIDIA, and Intel have each evolved tremendously since we started ArrayFire. Over a decade ago, NVIDIA and its CEO-founder Jensen saw the opportunity to teach the world a new heterogeneous model of computing that overwhelmingly convinces scientists, engineers, and analysts ...

I am AI at NVIDIA & ArrayFire

John Melonakos AI, ArrayFire, Events Leave a Comment

I am an explorer.I am a helper.I am a healer.I am a visionary.I am a builder.I am even the narrator of the story you are watching.And the composer of the music.I am AI. These words are from the first 3:11 min of Jensen's keynote today. Yesterday was another amazing NVIDIA GTC kicking off. Fully remote due to coronavirus, I still enjoyed the content without the travel. I encourage you to watch the video below. A masterpiece. ArrayFire has participated and exhibited at every in-person NVIDIA GTC, ever since the 2008 NVIDIA NVISION conference, click the link for a nice flashback. NVIDIA has come a long way from that 0:37 clip and the 3:11 clip Jensen showed above. At NVISION, we ...

ArrayFire v3.7.x Release

Stefan Yurkevitch Announcements, ArrayFire Leave a Comment

With the release of the 3.7.2 patch release, we wanted to discuss some of the major features added to ArrayFire. The binaries have been available for a few weeks but we wanted to discuss the changes here. It can be downloaded from these locations: Official installers GitHub repository This version of ArrayFire is better than ever! We have added many new features that expand the capabilities of ArrayFire while improving its performance and flexibility. Some of the new features include: 16-bit floating point support Neural network compatible convolution and gradient functions Reduce-by-key Confidence Connected Components Array padding functions Support for sparse-sparse arithmetic operations Pseudo-inverse, meanvar(), rqsrt() and much more! We have also spent a significant amount of effort exposing the ...

ArrayFire v3.6.4

Umar Arshad ArrayFire 1 Comment

We are proud to announce another exciting release of the ArrayFire library. This version fixes a critical performance regression in the ArrayFire just in time kernel generation code. We discovered the regression late in the release window for the v3.6.3 release and we couldn't address it in the previous version so this version only consists of 2 commits. Please go check out the page for the latest installers.

ArrayFire v3.6.2 Release

Stefan Yurkevitch ArrayFire Leave a Comment

We are excited to announce ArrayFire v3.6.2! In this release we have fixed a number of bugs, improved documentation, and added a few features while improving performance as always. We highlight some of the exciting changes below. New features and improvements Several features added in v3.6.2 are concerned with batching and broadcasting. In v3.6 we introduced batched matmul() allowing you to multiply several matrices at the same time. You could only batch arrays that were the same shape and size on the left hand side and the right hand size. In cases when you wanted to multiply one matrix with multiple matrices, you had to tile the inputs so that they were the same shape before you performed the multiplication. ...