• Additional Modules

    Expand the capabilities of AutoQuant X3

     

    AutoQuant X3 GPU Module

    Optimize your Time

    Deconvolution Times vs. Image Size
    Image Size
    Deconvolution Time
    GPU GTX1070 GPU GTX970 CPU 2x E5506

    Faster Processing, Same Quality

    The best known and most trusted deconvolution package is now the most affordable GPU based platform on the market. Utilizing the Graphics Processing Unit rather than the Central Processing Unit, will allow the user to experience the same quality results in only a fraction of the time.

    Upgrade your existing AutoQuant package or find out how to purchase your copy of AutoQuant GPU.

    Easy Implementation

    Faster Response, Same Easy to Use Interface

    AutoQuant X3 GPU Module delivers even faster response times than AutoQuant X3 without sacrificing the ease of use that is inherent in the AutoQuant Platform. This removes the need for extensive additional training of users and removes the downtime to obtain immediate results.

    With a single click, switch between CPU based to GPU based deconvolution, increase your response time, decrease your training time, and get the full benefit of AutoQuant X3 GPU module.

    Easily Export to and from Image-Pro Premier

    Supported Modalities

    ModalityWidefieldConfocalConfocal and Widefield
    Brightfield  
    Widefield Fluorescence  
    Structured Illumination  
    Light Sheet (SPIM)  
    Confocal Fluorescence  
    Multi-Photon  
    Spinning Disk  
    STED  

    = Supported in Module

    = Measured PSF

     

    GPU Deconvolution

    Understanding How it all Works

    Although many in the industry refer to this as GPU based deconvolution out of necessity, certain portions of the deconvolution process still occur on the CPU, notably the initial setup. Once this is complete, AutoQuant accesses the many GPU processor cores that carry out the actual iterative deconvolution. The final results are then transferred back to CPU and saved.

    Autoquant GPU

    Graphics Processing Units (GPUs) have evolved from simple display adapters into computational powerhouses for applications like image processing. As rendering demands have increased, specialized processors on the GPU became more generalized, faster, and more numerous. These changes culminated in the rise of general-purpose computation being performed on devices that had nominally been designed to make special effects in games and movies look as realistic as possible.

    The individual processor cores on a GPU are considerably slower than most CPU processor cores, but what they lack in power, they more than make up in numbers. The core count on a high-end workstation’s CPU(s) will be typically be around 32. In contrast, the core counts reported on even consumer-grade GPUs number into the thousands.


    CPU
    MULTIPLE CORES


    GPU
    THOUSANDS OF CORES

    For computationally-intensive applications like deconvolution, in which a series of mathematical calculations must be performed on each one of millions of individual voxels, being able to process thousands at once is preferable to processing a few at a time. In short, you have more bandwidth available when you utilize the GPU rather the CPU allowing for faster processing of the image.

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