Fp64 props7/8/2023 PIConGPU is a highly optimized application that runs production jobs at scale on a system Oak more » Ridge Leadership Facility’s (OLCF) Summit supercomputer (using the full machine at 4600 nodes at 98% of GPU utilization on all ~28000 NVIDIA Volta GPUs). While PIConGPU has been optimized for at least 5 years to run well on NVIDIA GPU-based clusters, there has been limited exploration by the development team of potential scalability bottlenecks using recently updated and new tools including NVIDIA’s NVProf tool and the brand-new NVIDIA NSight Suite (Systems and Compute) tools. PIConGPU, Particle In Cell on GPUs, is an open source simulations framework for plasma and laser-plasma physics used to develop advanced particle accelerators for radiation therapy of cancer, high energy physics and photon science. Our experiences point to an expanding arena for GPU vendors in HPC for molecular = , We find that in general, performance is competitive and installation is straightforward, even at these early stages in a new GPU ecosystem. These programs are used extensively in industry for pharmaceutical and materials research, as well as academia, and are also frequently deployed on high-performance computing (HPC) systems, including national leadership HPC resources. Here we test the ports of several widely used molecular dynamics packages that have each made substantial use of acceleration with NVIDIA GPUs, on Spock, the early Cray pre-Frontier testbed system at the OLCF which employs AMD GPUs. The future LUMI supercomputer in Finland will be based on an HPE Cray EX platform as well. The Hewlett Packard Enterprise (HPE) Cray EX Frontier supercomputer installed at the Oak Ridge Leadership Computing Facility (OLCF) will provide an exascale resource for open science, and will feature graphics processing units (GPUs) from Advanced Micro Devices (AMD). Simulating molecular dynamics requires extremely rapid cal- culations to enable sufficient sampling of simulated temporal molecular processes. If you mainly deploy into a know set of clients that you can test in advance, this is not a big issue, however if you expect your applications to work across a large set of devices you may want to stay with 32-bit calculations.Molecular simulation is an important tool for nu- merous efforts in physics, chemistry, and the biological sciences. The fp64 shader module has been tested on a range of GPUs and drivers however every now and then we encounter a new driver that needs special treatment. Other Considerations Ħ4-bit shaders push the GPU drivers quite a bit, and workarounds are needed to prevent GPU drivers from optimizing away critical parts of the code. Shaders are more complex and can take time to compile on some systems, notably Windows.įor more information regarding the performance of 64-bit layers, please check the performance benchmark layers in the layer-browser example in deck.gl repo. Same as mentioned above, since a layer usually has some attributes that do not require 64-bit maths, the total memory impact is normally significantly less than 2x. There will be a memory impact too, in that all vertex attributes and uniform that uses 64-bit maths require double storage space in JavaScript. However, since 64-bit floating point maths are usually only required in vertex shader, the overall performance impact is usually less than 10x. Math (the shader execution time alone is about 10x slower). Since the 64-bit floating point maths are emulated using the multiple precision arithmetics, it costs more GPU cycles than native 32-bit Note: ulp = unit of least precision Performance Implications The error bound as tested on 2015 MacBook Pro with AMD Radeon R9 M370X GPU: Operation Generally speaking, this mechanism provide 46 significant digits in mantissa (48 overall) within the normal range of 32-bit single precision float point numbers. Since WebGL does not support 64-bit floating point, deck.gl uses two 32-bit native floating point number to extend and preserve significant digits and uses algorithms similar to those used in many multiple precision math libraries to achieve the precision close to what IEEE-754 double precision floating point numbers provide. Points covering a whole city and accurate down to sub-centimeter level can be processed and rendered to canvas on-the-fly. With 64-bit floating point support in shader, deck.gl layers is able to visualize data with very high dynamic range. 32-bit vs 64-bit Mandelbrot Set Zoom Mandelbrot set rendered on GPU using native 32-bit (left) floating point shaders and emulated 64-bit (right) floating point shaders provided by deck.gl.
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