HPC in Medical Applications

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Medical applications like CT (computed tomography) scanning and MRI (magnetic resonance imaging) require quick, accurate results from processing complex algorithms. So reducing the compute time required is a primary challenge to manufacturers of CT and MRI equipment. Other significant challenges include the cost of the computers required to achieve the necessary performance and the space those computers occupy. Two recent technical advances have significantly helped to overcome these issues: the adoption of PCI Express (PCIe) over cable and the almost simultaneous emergence of compute acceleration cards (GPUs).

Mark Gunn, Sr. VP, One Stop Systems

Mark Gunn, Sr. VP, One Stop Systems

PCIe first emerged as the bus of choice in 2005. Among its many advantages is that the PCIe bus can be transmitted over a cable to another device.  This set the stage for the introduction of many expansion appliances that connect directly to a host computer through the PCIe bus. The expansion enclosure is one such device that provides external slots that the server can access as if they are in the server itself. Expansion enclosures support a multitude of commercially available PCIe add-in boards. Because these boards are operating on the same PCIe bus as the motherboard, no software conversion is required from the server to the device, thus reducing latency and making PCIe more attractive than Infiniband or other high speed cabled buses.

About the same time that the PCIe bus emerged, GPU cards began to be used for general compute acceleration. Multi-core GPU processors significantly offload the CPU, delivering results more quickly and reducing the workload on the CPU. Results that took hours to compute using traditional CPUs are now delivered in record time. Medical imaging is one of the earliest applications to take advantage of GPU computing to achieve acceleration. The use of GPUs in this field has matured to the point that there are a number of medical devices shipping with multiple AMD or NVIDIA Tesla GPUs. Other medical devices employing multiple GPUs in this way are microsurgery robots, wedge prism endoscopes, and surgical stereoscopic composite displays.


Figure 1. Ultrasound using GPU technology

Most computers do not provide enough power or cooling to accommodate multiple GPUs. PCIe expansion appliances supporting multiple GPUs have begun to be used in these applications, thus reducing the number of servers required. The fewer servers required, the lower the overall cost and the reduction of space necessary to accommodate them. In addition, GPU appliances can often be cabled to more than one server, spreading the workload out more efficiently. For example, a 2U GPU appliance with four GPUs can be cabled to one or two servers.  Each connection has a 128Gb/s throughput with additional PCIe switches to allow each GPU to operate at full bandwidth.  Using four NVIDIA Tesla K80 GPUs, the CA4000, a 2U GPU appliance from One Stop Systems delivers 35Tflops of computational power.


Figure2. The CA4000 GPU appliance from OneStop Systems adds almost 20,00 cores and 35 TFlops of compute power to one or two servers

A computing system using the NVIDIA Tesla GPUs gives a CT scan system the horsepower it needs to meet the healthcare industry’s pace. A configuration of four Tesla GPU processors is able to run through a scanner’s algorithm in less than 20 minutes. By comparison, a 16-processor computer system takes more than twice the time. In addition, a single server with a GPU appliance with four Tesla GPUs is considerably less expensive than the 16-node cluster.


Figure 3. Whole Breast Ultrasound (WBU™) system from Techniscan

This significantly reduces the overall equipment cost. By using NVIDIA’s GPU computing technology in Techniscan’s WBU, radiologists can perform a complete ultrasound scan and see the results within a 30-minute patient visit. This eliminates the delay in test results so patients and doctors have a fast and efficient device that can be relied upon to deliver results at the pace of modern medicine.

Receiving results quickly and accurately from medical procedures is the primary concern of today’s clinicians. In order to accomplish this many applications are turning to a segment of computing known as high performance computing (HPC) for answers. The latest technologies in HPC are being utilized in medical systems. CT scanning, MRI, and ultrasound are examples that incur a number of challenges to meet these requirements.  Among these are reducing time, cost, and space. By using PCIe expansion to add multiple GPUs to a system topology, significantly reduces the time it takes for a patient to receive diagnostic results. Fewer servers are required to achieve the required performance and fewer servers means less overhead, reducing costs and saving valuable space.

This article was written by Mark Gunn, Senior Vice President, One Stop Systems.

NVIDIA Tesla K80 is a registered trademark of the NVIDIA Corporation.

OSS CA4000 is a trademark of One Stop Systems, Inc.