The right ventricular (RV) function deterioration strongly predicts clinical outcomes in numerous circumstances. To boost the clinical deployment of ensemble regression methods that quantify RV volumes using tabular data from the widely available two-dimensional echocardiography (2DE), we propose to complement the volume predictions with uncertainty scores. To this end, we employ an instance-based method which uses the learned tree structure to identify the nearest training samples to a target instance and then uses a number of distribution types to more flexibly model the output. The probabilistic and point-prediction performances of the proposed framework are evaluated on a relatively small-scale dataset, comprising 100 end-diastolic and end-systolic RV volumes. The reference values for point performance were obtained from MRI. The results demonstrate that our flexible approach yields improved probabilistic and point performances over other state-of-the-art methods. The appropriateness of the proposed framework is showcased by providing exemplar cases. The estimated uncertainty embodies both aleatoric and epistemic types. This work aligns with trustworthy artificial intelligence since it can be used to enhance the decision-making process and reduce risks. The feature importance scores of our framework can be exploited to reduce the number of required 2DE views which could enhance the proposed pipeline's clinical application.

The first instances to include NVIDIA Tesla V100 GPUs, P3 instances are the most powerful GPU instances available in the cloud. P3 instances allow customers to build and deploy advanced applications with up to 14 times better performance than previous-generation Amazon EC2 GPU compute instances, and reduce training of machine learning applications from days to hours. With up to eight NVIDIA Tesla V100 GPUs, P3 instances provide up to one petaflop of mixed-precision, 125 teraflops of single-precision, and 62 teraflops of double-precision floating point performance, as well as a 300 GB/s second-generation NVIDIA NVLink interconnect that enables high-speed, low-latency GPU-to-GPU communication. P3 instances also feature up to 64 vCPUs based on custom Intel Xeon E5 (Broadwell) processors, 488 GB of DRAM, and 25 Gbps of dedicated aggregate network bandwidth using the Elastic Network Adapter (ENA). "When we launched our P2 instances last year, we couldn't believe how quickly people adopted them," said Matt Garman, Vice President of Amazon EC2.

Powerful and cost-effective HPC platforms promote data fusion, reduce training time, and enable ultra-scale real-time data analytics to power deep learning systems. In today's digital climate, organizations of every size and industry are both collecting and generating enormous amounts of data that can potentially be used to solve the world's greatest problems--from national security and fraud detection to scientific breakthroughs and technological advancement. However, traditional analysis techniques and practices are not capable of rapidly delivering automated, real-time insights from the rising data volumes to the point that artificial intelligence (AI) is becoming vital to harnessing the full understanding of scientific and business data. The evolution of Big Data is driving a major paradigm shift in the field of AI, which is increasing the need for high performance computing (HPC) technologies that can support high performance data analytics (HPDA). According to an IDC report, the HPDA server market is projected to grow at a 26% CAGR through 2020, including an additional $3.9 billion in revenue by 2018.

With up to 16 NVIDIA Tesla K80 GPUs, P2 instances are the most powerful GPU instances available in the cloud. "The massive parallel floating point performance of Amazon EC2 P2 instances, combined with up to 64 vCPUs and 732 GB host memory, will enable customers to realize results faster and process larger datasets than was previously possible." P2 instances allow customers to build and deploy compute-intensive applications using the CUDA parallel computing platform or the OpenCL framework without up-front capital investments. To offer the best performance for these high performance computing applications, the largest P2 instance offers 16 GPUs with a combined 192 Gigabytes (GB) of video memory, 40,000 parallel processing cores, 70 teraflops of single precision floating point performance, over 23 teraflops of double precision floating point performance, and GPUDirect technology for higher bandwidth and lower latency peer-to-peer communication between GPUs. P2 instances also feature up to 732 GB of host memory, up to 64 vCPUs using custom Intel Xeon E5-2686 v4 (Broadwell) processors, dedicated network capacity for I/O operation, and enhanced networking through the Amazon EC2 Elastic Network Adaptor.

With up to 16 NVIDIA Tesla K80 GPUs, P2 instances are the most powerful GPU instances available in the cloud. P2 instances allow customers to build and deploy compute-intensive applications using the CUDA parallel computing platform or the OpenCL framework without up-front capital investments. To offer the best performance for these high performance computing applications, the largest P2 instance offers 16 GPUs with a combined 192 Gigabytes (GB) of video memory, 40,000 parallel processing cores, 70 teraflops of single precision floating point performance, over 23 teraflops of double precision floating point performance, and GPUDirect technology for higher bandwidth and lower latency peer-to-peer communication between GPUs. P2 instances also feature up to 732 GB of host memory, up to 64 vCPUs using custom Intel Xeon E5-2686 v4 (Broadwell) processors, dedicated network capacity for I/O operation, and enhanced networking through the Amazon EC2 Elastic Network Adaptor. Two years ago, we launched G2 instances to support customers running graphics and compute-intensive applications," said Matt Garman, Vice President, Amazon EC2. "Today, as customers embrace heavier GPU compute workloads such as artificial intelligence, high-performance computing, and big data processing, they need even higher GPU performance than what was previously available.

Amazon Web Services (AWS) has unveiled a new GPU-powered cloud computing service for artificial intelligence, seismic analysis, molecular modeling, genomics, and other applications that need vast amounts of parallel processing power. AWS said its P2 instances for Amazon Elastic Compute Cloud (Amazon EC2) are aimed at applications that require "massive parallel floating point performance" . "These instances were designed to chew through tough, large-scale machine learning, deep learning, computational fluid dynamics, seismic analysis, molecular modeling, genomics, and computational finance workloads," said Jeff Barr, chief evangelist at AWS. While GPUs were first associated with gaming, they're now finding a new life in dealing with huge computing workloads, as they can be scaled out so that banks of GPUs handle tasks in parallel. This is in contrast to the traditional approach of scaling up, where increasingly complex problems were tackled using individual machines with ever faster CPUs, which is becoming increasingly hard to do.

From ISC 2016 in Frankfurt, Germany, this week, Intel Corp. launched the second-generation Xeon Phi product family, formerly code-named Knights Landing, aimed at HPC and machine learning workloads. The company had been shipping "Knights Landing" silicon to early customers for the last six months and was waiting to ramp up production before making the product generally available. The window also gave OEMs time to complete their readiness, said Intel's Charlie Wuischpard, vice president of the Data Center Group and general manager of High Performance Computing Platform Group, in a media pre-briefing. Those OEMs include the usual names: Cray, HPE, Lenovo, Dell and others. The most distinguishing feature of the chip is that it's a bootable host CPU -- unlike its predecessor "Knights Corner," which is a coprocessor that connects over PCIe.

If you want to get under Diane Bryant's skin these days, just ask her about GPUs. The head of Intel's data center group was at Computex in Taipei this week, in part to explain how the company's latest Xeon Phi processor is a good fit for machine learning. Machine learning is the process by which companies like Google and Facebook train software to get better at performing AI tasks including computer vision and understanding natural language. It's key to improving all kinds of online services: Google said recently that it's rethinking everything it does around machine learning. "It's a big opportunity, and there will be a hockey stick where every business will be using machine learning," she said in an interview.

If you want to get under Diane Bryant's skin these days, just ask her about GPUs. The head of Intel's powerful data center group was at Computex in Taipei this week, in part to explain how the company's latest Xeon Phi processor is a good fit for machine learning. Machine learning is the process by which companies like Google and Facebook train software to get better at performing AI tasks including computer vision and understanding natural language. It's key to improving all kinds of online services: Google said recently that it's rethinking everything it does around machine learning. "It's a big opportunity, and there will be a hockey stick where every business will be using machine learning," she said in an interview.