F# is a functional first language. It is cross platform, open-source with professional development tools and backed by an active community. The popularity of F# steadily increased. It is used in specialized areas such as scientific computing, data science, machine learning, or for trading and risk applications.
F# is indeed a very compelling technology. But is it also suitable for main stream and enterprise application development? What are the advantages of F# over C#? When should I use F# and what would be the business value of adopting it? In this talk we look at F# and its ecosystem more from a strategic point of view. We highlight the strength and unique features of F# and how they can be used to build sophisticated enterprise applications. We illustrate the business relevance of F# with concrete projects and solutions that we completed over the last few years.
GPU cloud computing is gaining more and more momentum because a growing number of applications and use cases rely on fast enterprise GPU hardware, such as deep learning, applied to image and speech recognition or natural language processing, data mining, photo-realistic real-time rendering, etc. Some of these applications also benefit from scaling to multi-GPU servers and to GPU server clusters with high speed network interconnects.
Public cloud market leader Amazon AWS was the first to provide GPU accelerated instances. Its g2.2xlarge instance has 8 CPU core and 1 GRID K520 GPU, the larger g2.8xlarge instance has 32 CPU cores and 4 GRID K520 GPUs, available on demand at hourly rates.
This summer IBM SoftLayer launches a new server line with Nvidia Tesla K80 GPUs. And just a few days later the Chinese e-commerce giant Alibaba announced GPU support on its Aliyun public cloud.
Finally last Tuesday at AzureCon 2015, Microsoft announced the new N-series GPU-enabled virtual machines. They use K80 GPUs for compute intensive workloads and M60 GPUs with the recently released Nvidia Grid 2.0 technology for virtualized graphics. With this hardware infrastructure Microsoft Azure can now deliver GPU accelerated computations and graphics to any connected device.
The new GPU cloud infrastructure of Azure uses GPU virtualization with Hyper-V and DDA technology. Fast RDMA network connections can be used to build clusters of multiple GPU nodes for HPC workloads.
and Jen-Hsun Huang who speaks about GPUs for the Azure Cloud
For us at QuantAlea this is great news. We are already in the development process of Alea GPU V3, which will be yet another major release of our GPU compiler infrastructure for .NET, C# and F#. The key features of V3 will be a clever new memory management system, a simplified user interface and API so that also novice C# developers can easily write GPU code and of course a tight integration with the Azure GPU infrastructure and higher level Azure cloud services.
These news also had a very positive effect on the share price of Nvidia, which gained 2.7% closing at USD 24.36, hitting a 52-week high of USD 24.58 on Wednesday after the announcement. With all these events in just a few months the blog published early July this year on The Platform can be extended with a few more sections.
We are curious to see how and when Google will enhance his public cloud with GPU computing capabilities.