website<\/a>, AWS’ 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.<\/p>\n“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.”<\/p>\n
Just plain cool use cases:<\/strong><\/p>\nClarifai provides image and video recognition APIs\u00a0<\/strong>or some of the world\u2019s most innovative companies:<\/p>\n\u201cDeep learning plays a central role in our image and video classification APIs, and high performance GPUs in the AWS Cloud can vastly accelerate inference for our algorithms,\u201d said Matthew Zeiler, CEO, Clarifai. \u201cAmazon EC2 P2 instances will give us the agility to scale up to serving numerous models in parallel, delivering results faster than previously possible without massive capital expenditures, un-utilized hardware, and large data transfers. We will be able to leverage the massive aggregate single precision floating point processing capability of Amazon EC2 P2 instances to reduce inference times for our customers, and substantially reduce the cost of processing.\u201d<\/p>\n
MathWorks, the leading developer of mathematical computing software<\/strong>, helps millions of engineers, scientists, researchers, and students around the world analyze and design systems and products that are transforming the world:<\/p>\n\u201cMATLAB users moving their analytics and simulation workloads onto the AWS Cloud require their analyses to be processed quickly,\u201d said Silvina Grad-Freilich, Senior Product Manager, MathWorks. \u201cThe 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.\u201d<\/p>\n
MapD is a GPU database for interactive SQL querying and visualization of multi-billion record datasets:<\/strong><\/p>\n\u201cAs the leader in GPU-powered databases and visual analytics applications, we are deeply invested in the emergence of large, cloud-based GPU instances and P2 is the most powerful we have seen,\u201d said Todd Mostak, CEO and Founder, MapD. \u201cOur performance on Amazon EC2 P2 instances is exceptional. On a dollar-to-dollar basis across a set of standard SQL benchmarks, MapD is 78 times faster on Amazon EC2 P2 instances than CPU-based solutions. Furthermore, these speedups were seen over multi-billion row datasets, speaking directly to our ability to deliver performance at scale with these instances. With this launch, our customers can now query and visualize billions of rows of data within milliseconds while enjoying the flexibility, scalability and reliability they have come to expect from AWS.\u201d<\/p>\n
The growing need for GPU compute workloads within AI, and data sets as large as those mentioned previously, \u00a0users need even higher GPU performance than what was previously available. Matt Garman, Vice President of Amazon EC2, explains that the beauty of P2 instances, is that AWS offers seven times the computational capacity for single precision floating point calculations and 60 times more for double precision floating point calculations than the largest G2 instance:<\/p>\n
“This allows top performance for compute-intensive workloads such as financial simulations, energy exploration and scientific computing.\u201d<\/p>\n
To learn more about AWS, visit https:\/\/aws.amazon.com<\/a>.<\/p>\n
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