1/6/2024 0 Comments Amazon dim3 direction![]() We can exploit the large regions of uniform dwell using the hierarchical Mariani-Silver algorithm to focus computation where it is most needed. In general, the only areas where we need high-resolution computation are along the fractal boundary of the set. There are also large regions of constant but low dwell outside the Mandelbrot set. There are large regions inside the set that could simply be colored black note that since MAX_DWELL iterations are performed for every black pixel, this is where the algorithm spends most of the computation time. However, under closer examination, we can see that such an algorithm wastes a lot of computational resources. This kernel runs quickly on the GPU it can compute a 4096×4096 image with MAX_DWELL of 512 in just over 40 ms on an NVIDIA Kepler K20X accelerator. _host_ _device_ int pixel_dwell(int w, int h,įloat fx = (float)x / w, fy = (float)y / h Ĭomplex c = cmin + complex(fx * dc.re, fy * dc.im) cmin, cmax - coordinates of bottom-left and top-right image corners w, h - width and hight of the image, in pixels We compute the dwell for a single pixel using the following code. In each iteration (up to a predefined maximum), the algorithm modifies the point according to the Mandelbrot set equations and checked to see whether it “escapes” outside a circle of radius 2 centered at point (0, 0). For each pixel in the image, the escape time algorithm computes the value dwell, which is the number of iterations it takes to decide whether the point belongs to the set. The most common algorithm used to compute the Mandelbrot set is the “escape time algorithm”. The interior of Figure 2 (the black part), is the Mandelbrot Set. The Mandelbrot set is perhaps the best known fractal. Case Study: PANDA – how Dynamic Parallelism made it easier and more efficient to implement Triplet Finder, an online track reconstruction algorithm for the high-energy physics PANDA experiment, which is part of the Facility for Antiproton and Ion Research in Europe (FAIR).API and Principles – Advanced topics in Dynamic Parallelism, including device-side streams and synchronization.Adaptive Parallel Computation – Dynamic Parallelism overview and example (this post).This is the first of a three part series on CUDA Dynamic Parallelism: ![]() This post introduces Dynamic Parallelism by example using a fast hierarchical algorithm for computing images of the Mandelbrot set.
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