Qualcomm investigating Adreno X2 support for Folding@Home

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Ncard00
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Qualcomm investigating Adreno X2 support for Folding@Home

Post by Ncard00 »

Qualcomm is now reviewing the technical feasibility of bringing the necessary compute stack to the Snapdragon X2 for FAH.

They are aware of the GROMACS requirements and are looking at how to implement the precision needed for molecular dynamics.
toTOW
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Re: Qualcomm investigating Adreno X2 support for Folding@Home

Post by toTOW »

GROMACS on GPUs sucks :roll: It's still too dependent on the CPU. :?

And it will already run on ARM CPUs (although big.LITTLE shit is always a pain).
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Folding@Home beta tester since 2002. Folding Forum moderator since July 2008.
Ncard00
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Re: Qualcomm investigating Adreno X2 support for Folding@Home

Post by Ncard00 »

toTOW wrote: Fri Feb 20, 2026 4:40 pm GROMACS on GPUs sucks :roll: It's still too dependent on the CPU. :?

And it will already run on ARM CPUs (although big.LITTLE shit is always a pain).
Update: Breakthrough in Adreno GPU Compute Stability

Since my last post, I have moved from theoretical feasibility to verified results. I have successfully bypassed current software limitations and executed large-scale scientific compute tasks natively on the Adreno X1-45 iGPU (Snapdragon X Elite) using OpenCL 3.0.

Contrary to the typical "mobile GPU" skepticism, the Adreno X1 platform demonstrates desktop-class stability and performance in long-term compute scenarios:

Proof of Performance: I successfully ran PrimeGrid GFN-22 on the Adreno iGPU, completing tasks in 33 hours compared to a 14-day estimate on the Oryon CPU.

GPU Utilization: Maintained 100% sustained load on the Adreno core without driver crashes or TDR issues, proving the compute stack is ready for more than just mobile tasks.

Benchmarking: GFN-16 tasks completed at a stable 04:36 minutes.

While GROMACS is indeed CPU-dependent, my results show that the Adreno iGPU can handle massive mathematical workloads independently when properly addressed via OpenCL.

I have also identified the presence of the Qualcomm Native Neural Network (QNN) interface (QnnHtp.dll) and the Hexagon Tensor Processor (HTP) driver, which opens up for potentially massive efficiency gains in molecular dynamics if bridged correctly via DirectML or QNN.

The hardware is no longer the bottleneck—the whitelist and official support are all that remain.
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Re: Qualcomm investigating Adreno X2 support for Folding@Home

Post by muziqaz »

Please demonstrate the stability of the software and hardware on openmm. It has it's own benchmarking suite, which can be used to compare performance wise to other hardware as well as test the computational stability.
Other distributed computing projects' stability is irrelevant to FAH
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