R9 270 fp64 driver#
R9 270 fp64 pro#

Each precision has two modes, explicit and implicit, the difference being whether water atoms are included in the simulation, which adds quite a bit of work and overhead. FAHBench can test both single precision and double precision floating point performance, with single precision being the most useful metric for most consumer cards due to their low double precision performance. Folding Home is the popular Stanford-backed research and distributed computing initiative that has work distributed to millions of volunteer computers over the internet, each of which is responsible for a tiny slice of a protein folding simulation. Moving on, our 4th compute benchmark is FAHBench, the official Folding Home benchmark. The Fury X none the less shaves off an additional second of rendering time, bringing it down to 21 seconds. This specific test comes from Sony, and measures how long it takes to render a video.Īt this point Vegas is becoming increasingly CPU-bound and will be due for replacement. With video encoding being increasingly offloaded to dedicated DSPs these days we’re focusing on the editing and compositing process, rendering to a low CPU overhead format (XDCAM EX). Vegas can use GPUs in a few different ways, the primary uses being to accelerate the video effects and compositing process itself, and in the video encoding step.

R9 270 fp64 pro#

Our 3rd compute benchmark is Sony Vegas Pro 13, an OpenGL and OpenCL video editing and authoring package. Overall then the outcome over who wins is heavily test dependent, though this is nothing new. The Fury X only wins overall at Optical Flow, a somewhat memory-bandwidth heavy test that to no surprise favors AMD’s HBM additions, but otherwise the performance gains across all of these tests are 40-50%.

R9 270 fp64 driver#

Quickly taking some of the air out of our driver theory, the R9 Fury X’s performance on CompuBench is quite a bit better, and much closer to what we’d expect given the hardware of the R9 Fury X. CompuBench offers a wide array of different practical compute workloads, and we’ve decided to focus on face detection, optical flow modeling, and particle simulations. Given the fact that the R9 Fury X is first and foremost a gaming card, and OpenCL 1.x traction continues to be low, I am wondering whether we’re seeing a lack of OpenCL driver optimizations for Fiji.įor our second set of compute benchmarks we have CompuBench 1.5, the successor to CLBenchmark.

It just can’t do so very well with LuxMark. Right now I have no reason to doubt that the R9 Fury X is capable of utilizing all of its shaders. And yet while the R9 Fury X is the fastest single GPU AMD card, it’s only some 16% faster, a far cry from the 50%+ that it should be able to attain. Compute workloads are shader workloads, and these are workloads that should best illustrate the performance improvements of R9 Fury X over R9 290X. The results with LuxMark ended up being quite a bit of a surprise, and not for a good reason. Ray tracing has become a stronghold for GPUs in recent years as ray tracing maps well to GPU pipelines, allowing artists to render scenes much more quickly than with CPUs alone. LuxRender’s GPU-accelerated rendering mode is an OpenCL based ray tracer that forms a part of the larger LuxRender suite. Starting us off for our look at compute is LuxMark3.0, the latest version of the official benchmark of LuxRender 2.0. On the other hand with a theoretical FP32 performance of 8.6 TFLOPs, AMD could really clean house on our more regular FP32 workloads. As an FP64 card, the R9 Fury X only offers the bare minimum FP64 performance for a GCN product, so we won’t see anything great here. Shifting gears, we have our look at compute performance.