How is GPU Cloud Critical to AI?

Medical Imaging is a field where the need for technological innovation cannot be stressed more. As we face a shortage of radiologists and consequent rise in turn around time for diagnosis results the challenges appear to be more evident. Cloud computing and AI offer innovative solutions to tackle these deficiencies. The whole field of healthcare can get an uplift by making use of GPU super computing innovations. But with medical imaging, it is not all that simple to go on the cloud.

Medical imaging is the use of several difference technologies and technique to generate images of body parts, tissues, or organs for use in clinical diagnosis, treatment, and disease monitoring. Technology is being transformed at a very faster pace in the healthcare industry. Medical imaging has been positively affected by this technological disruption. This has been enabled by key developments in the artificial intelligence (AI). Artificial intelligence is the use of computer systems to perform tasks that normally require human intelligence. Through deep learning, computers can construct a wide array of algorithms that can to provide robust and powerful GPU computation for data modeling.

AI-based medical imaging relies on a vast supply of medical case data to train its algorithms to find patterns in images and identify specific anatomical markers. Through rigorous analysis of patterns in a given digital image, the imaging algorithms can derive metrics and output that complement the analyses made by the radiologist, which can be useful for quick diagnosis.

AI algorithms has a distributed pattern. Deep neural network and AI in machine learning can be categorized as parallel problems which means parallel super computing solutions like GPUs can speed up 90%. GPUs are best suited for speeding up distributed pattern of AI algorithms where each unit in distributed system works independent of the other units. A neural network will learn several times faster on a GPU than a CPU.

Artificial Intelligence AI and machine learning play a vital role in continuing competitive advantage and delivering fantastic user experience. GPU is very precious as it accelerates the tensor processing necessary for deep learning applications. A GPU has its own memory that keeps the whole graphics image as a matrix. GPU calculates change in the image using tensor math, whenever any change is made to the image like adding color to the pixel, GPU performs this process much faster instead of redrawing the entire screen every time the image changes. These deep learning approaches have shown impressive performances in resembling humans in various fields, including medical imaging.

Cloud giants are not focused on delivering visualizations of AI results

Medical imaging is associated with high degree latency-sensitive applications and global leaders of the cloud are not able to provide this domain-specific essential requirement. Amazon, Google, Microsoft, Oracle and the rest developed their clouds for long term storage of data and are not necessarily good at holding Big Data for instantaneous interactivity. Medical imaging requires high powered processing techniques.

GPU – Taking computing to another level

GPUs or Graphics Processing Units help deliver high-quality medical images. They are highly effective for deep learning. They are 3000x faster than CPU in processing and can run tasks in a parallel processing manner. They bring down the processing time. GPUs were initially meant for 3D visual effects and gaming applications. However, the computational and convenience offered by these units have opened up possibilities for varied domains. GPU cores have an SIMD (single instruction multiple data) architecture that is more advanced that CPUs and can run a number of tasks parallelly at a given time. GPUs offer high bandwidth while hiding their latency under thread parallelism makes GPU a lot faster.

Medical Imaging is a field where the need for technological innovation cannot be stressed more. As we face a shortage of radiologists and consequent rise in turn around time for diagnosis results the challenges appear to be more evident. Cloud computing and AI offer innovative solutions to tackle these deficiencies. The whole field of healthcare can get an uplift by making use of GPU super computing innovations. But with medical imaging, it is not all that simple to go on the cloud.

Medical imaging is the use of several difference technologies and technique to generate images of body parts, tissues, or organs for use in clinical diagnosis, treatment, and disease monitoring. Technology is being transformed at a very faster pace in the healthcare industry. Medical imaging has been positively affected by this technological disruption. This has been enabled by key developments in the artificial intelligence (AI). Artificial intelligence is the use of computer systems to perform tasks that normally require human intelligence. Through deep learning, computers can construct a wide array of algorithms that can to provide robust and powerful GPU computation for data modeling.

AI-based medical imaging relies on a vast supply of medical case data to train its algorithms to find patterns in images and identify specific anatomical markers. Through rigorous analysis of patterns in a given digital image, the imaging algorithms can derive metrics and output that complement the analyses made by the radiologist, which can be useful for quick diagnosis.

AI algorithms has a distributed pattern. Deep neural network and AI in machine learning can be categorized as parallel problems which means parallel super computing solutions like GPUs can speed up 90%. GPUs are best suited for speeding up distributed pattern of AI algorithms where each unit in distributed system works independent of the other units. A neural network will learn several times faster on a GPU than a CPU.

Artificial Intelligence AI and machine learning play a vital role in continuing competitive advantage and delivering fantastic user experience. GPU is very precious as it accelerates the tensor processing necessary for deep learning applications. A GPU has its own memory that keeps the whole graphics image as a matrix. GPU calculates change in the image using tensor math, whenever any change is made to the image like adding color to the pixel, GPU performs this process much faster instead of redrawing the entire screen every time the image changes. These deep learning approaches have shown impressive performances in resembling humans in various fields, including medical imaging.

Cloud giants are not focused on delivering visualizations of AI results

Medical imaging is associated with high degree latency-sensitive applications and global leaders of the cloud are not able to provide this domain-specific essential requirement. Amazon, Google, Microsoft, Oracle and the rest developed their clouds for long term storage of data and are not necessarily good at holding Big Data for instantaneous interactivity. Medical imaging requires high powered processing techniques.

GPU – Taking computing to another level

GPUs or Graphics Processing Units help deliver high-quality medical images. They are highly effective for deep learning. They are 3000x faster than CPU in processing and can run tasks in a parallel processing manner. They bring down the processing time. GPUs were initially meant for 3D visual effects and gaming applications. However, the computational and convenience offered by these units have opened up possibilities for varied domains. GPU cores have an SIMD (single instruction multiple data) architecture that is more advanced that CPUs and can run a number of tasks parallelly at a given time. GPUs offer high bandwidth while hiding their latency under thread parallelism makes GPU a lot faster.

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