QUASAR: a new GPU programming environment
A simpler way to develop
universal, future-proof GPU code
QUASAR is a new programming environment for heterogeneous systems consisting of multi-core CPU and single/multi-GPU.
WHERE CAN IT BE USED?
Image/Video Processing and
HOW DOES QUASAR WORK?
Quasar features an integrated development environment called Redshift that is easy to learn, use and master. Quasar optimizes Redshift’s high-level code through two separate, inter-related components:
THE GPU COMPILER
translates the initial hardware-agnostic code so it can be used with GPU models of your choosing. With a modular approach that makes it easy to optimize for new GPUs as they are released, Quasar helps maintain algorithm performance and functionality over the long term—without having to rewrite the original code.
THE RUNTIME SUITE
looks at current load and other factors to determine which parts of code should run on the CPU and GPU for optimal program execution. The runtime suite also automatically handles many tasks programmers typically need to do manually, including scheduling, load balancing, and memory transfer and management.
WHAT ARE THE ADVANTAGES?
Rapid prototyping &
shorter GPU development time
QUASAR speeds up image reconstruction and video processing by 10 to 100 times through faster, easier programming of GPU/CPU parallel computing.
As an application specialist, you can focus on the specifics of the application rather than needing to spend time learning/optimizing code for the GPU architecture.
Fast execution using CUDA code generation
When testing algorithms, process video frames in 100's of microseconds rather than in seconds per frame. Make interactive changes to the parameters, rather than waiting for a simulation to finish.
Future proof GPU programming code
Hardware evolves at a very fast pace. Save time updating your algorithms each for new hardware architectures. Programming code is written once and can run on different platforms with different GPU devices. After updating Gepura/Quasar software, your software takes automatic advantage of new CPU/GPU features.
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QUASAR CPU & GPU PROGRAMMING CASE STUDIES
The following video demonstrates a real-time standard dynamic range (SDR) to high dynamic range (HDR) upconversion algorithm that was developed entirely in Quasar. The purpose of this algorithm is to display movies stored in SDR format on a HDR television. A user interface, also developed in Quasar, gives the user a few slides to choose…Read More
Simultaneous localization and mapping (SLAM) Martin Dimitrievski and his colleagues propose a novel real-time method for SLAM in autonomous vehicles. The environment is mapped using a probabilistic occupancy map model and EGO motion is estimated within the same environment by using a feedback loop. Input data is provided via a rotating laser scanner as 3D measurements…Read More
Magnetic Resonance Imaging (MRI) MRI is a very powerful and safe medical diagnostic tool, but it is prohibitively expensive to use frequently. Hence, a technique that speeds up MRI acquisition would not only be helpful for patients, as it requires them to lie still for shorter periods of time, but it would also be of great…Read More
Focal Black & White Effect A well known Google Picasa effect is the Focal Black & White Effect. This effect preserves the color within a focal region and converts pixels outside this region to grayscale. The algorithm is surprisingly simple: it consists of calculating a weighting factor (that depends on the focal radius), converting the pixel RGB…Read More
Optical Flow Simon Donné and his colleagues achieved significant speed-ups for Optical Flow, a widely used video analysis method, thanks to the use of GPUs and Quasar. Tracking an object through time is often still a hard task for a computer. One option is to use optical flow, which finds correspondences between two image frames: which pixel…Read More
In this post, we demonstrate some of the real-time video processing and visualization capabilities of Quasar. Initially, a monochromatic image and corresponding depth image were captured using a video camera. Because the depth image is originally quite noisy, some additional processing is required. To visualize the results in 3D, we define a fine rectangular mesh…Read More