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OpenCL In Action: Post-Processing Apps, Accelerated

Benchmark Results: vReveal On The A8-3850's Radeon HD 6550D

With discrete graphics fully benchmarked, we can switch over to the A8's integrated Radeon engine and compare.

With GPU acceleration enabled, we see a slight advantage from discrete graphics when handling 480p video.

The real shocker is that CPU utilization is almost identical when the integrated and discrete graphics subsystems work on 1080p video. The explanation is simple. A lighter load doesn't tax the Radeon HD 6550D, so you get real-time rendering from the APU. The implication is that you don't even need an add-in card to get these results, creating an opportunity for very mainstream users to see some pretty cool value.

But can the same conclusions be drawn when we test six effects rendering simultaneously?

Not quite. Because we have less graphics hardware to throw at this problem, CPU utilization turns out to be pretty similar whether you're looking at 480p or 1080p video. Turning on GPU-based acceleration clearly helps alleviate the workload, dropping our A8 to about 27% utilization at both resolutions.

However, the performance chart tells the second half of the story. Pushing CPU usage down is great at 480p, where host processing and graphics working together manage real-time rendering of six effects. But at 1080p, the two subsystems are collaboratively stuck at 29% of real-time. That's less than half of what the Radeon HD 5870 was able to do matched up to AMD's APU. For serious compute workloads, the sheer complexity of a discrete GPU is undeniably superior.

  • DjEaZy
    ... OpenCL FTW!!!
    Reply
  • amuffin
    Will there be an open cl vs cuda article comeing out anytime soon? :ange:
    Reply
  • Hmmm...how do I win a 7970 for OpenCl tasks?
    Reply
  • deanjo
    DjEaZy... OpenCL FTW!!!
    Your welcome.

    --Apple
    Reply
  • bit_user
    amuffinWill there be an open cl vs cuda article comeing out anytime soon?At the core, they are very similar. I'm sure that Nvidia's toolchain for CUDA and OpenCL share a common backend, at least. Any differences between versions of an app coded for CUDA vs OpenCL will have a lot more to do with the amount of effort spent by its developers optimizing it.
    Reply
  • bit_user
    Fun fact: President of Khronos (the industry consortium behind OpenCL, OpenGL, etc.) & chair of its OpenCL working group is a Nvidia VP.

    Here's a document paralleling the similarities between CUDA and OpenCL (it's an OpenCL Jump Start Guide for existing CUDA developers):

    NVIDIA OpenCL JumpStart Guide

    I think they tried to make sure that OpenCL would fit their existing technologies, in order to give them an edge on delivering better support, sooner.
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  • deanjo
    bit_userI think they tried to make sure that OpenCL would fit their existing technologies, in order to give them an edge on delivering better support, sooner.
    Well nvidia did work very closely with Apple during the development of openCL.
    Reply
  • nevertell
    At last, an article to point to for people who love shoving a gtx 580 in the same box with a celeron.
    Reply
  • JPForums
    In regards to testing the APU w/o discrete GPU you wrote:

    However, the performance chart tells the second half of the story. Pushing CPU usage down is great at 480p, where host processing and graphics working together manage real-time rendering of six effects. But at 1080p, the two subsystems are collaboratively stuck at 29% of real-time. That's less than half of what the Radeon HD 5870 was able to do matched up to AMD's APU. For serious compute workloads, the sheer complexity of a discrete GPU is undeniably superior.

    While the discrete GPU is superior, the architecture isn't all that different. I suspect, the larger issue in regards to performance was stated in the interview earlier:

    TH: Specifically, what aspects of your software wouldn’t be possible without GPU-based acceleration?

    NB: ...you are also solving a bandwidth bottleneck problem. ... It’s a very memory- or bandwidth-intensive problem to even a larger degree than it is a compute-bound problem. ... It’s almost an order of magnitude difference between the memory bandwidth on these two devices.

    APUs may be bottlenecked simply because they have to share CPU level memory bandwidth.

    While the APU memory bandwidth will never approach a discrete card, I am curious to see whether overclocking memory to an APU will make a noticeable difference in performance. Intuition says that it will never approach a discrete card and given the low end compute performance, it may not make a difference at all. However, it would help to characterize the APUs performance balance a little better. I.E. Does it make sense to push more GPU muscle on an APU, or is the GPU portion constrained by the memory bandwidth?

    In any case, this is a great article. I look forward to the rest of the series.
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  • What about power consumption? It's fine if we can lower CPU load, but not that much if the total power consumption increase.
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