NVIDIA have released the next generation of GRID 2.0. GRID 2.0 is based on the Maxwell architecture and the GRID 1.0 (K1/K2) was based on the Kepler architecture. I have been working with the GRID 1.0 technology since 2012 and it have matured alot in its 2 years of history. When the K1/K2 was released they was first working with GPU pass-through and then vGPU got introduced and you could virtualize the GPUs and increase density, which people wanted. Citrix was with their hypervisor the first company that supported NVIDIA GRID 1.0 and they was also the first company integrating vGPU into their Citrix Studio, so companies could easier provisioning machines with either MCS technology or PVS technology. VMware supported GRID 1.0 vGPU technology in 2015 in their hypervisor VMware vSphere 6.0 and fully integrated with their EUC stack VMware View, so companies can fully provisioning machines. The great thing about GRID 2.0 is that there is no need for a conversation when to choose either a K1 or a K2, if you required GPU compute or GPU framebuffer, M60 are being added to the tope end of the range and bringing 2x the performance, and if you have bladeserver’s, you can add the powerfull vGPU technology into the bladeserver’s with the M6.
Please notice that M6 will 0nly be supporting newer architecture of vendors not old platforms.
Maxwell architecture is the new architecture of GPUs and a powerful GPU you might know is the Titan X
In GRID 2.0 NVIDIA now have a GPU for blade servers a MXM single socket, High-end GPU called M6
In GRID 2.0 NVIDIA replaces K1/K2 with the new PCIe 3.0 Dual Socket, Dual High-end GPU called M60
The M60 delivers 4096 CUDA or compute and 16GB GDDR5 memory/framebuffer
The M60 has 6x the h.264 encoders of the K2, and also Maxwell supports 4:4:4 chroma sub sampling, which is great news for encoders.
Click the link to see which servers are certified for M60 and M6
NVIDIA GRID 2.0 software is available in three editions that deliver accelerated virtual desktops to support the needs of your users. These editions include Virtual PC, Virtual Workstation, and Virtual Workstation Extended. GRID perpetual licenses are sold by Concurrent User (CCU).
NVIDIA GRID 2.0 (CCU) stands for ConCurrent User. So basically, per running VM as regardless of whether the user is connected to the VM or not, the VM is connected to the GPU and so consumes a license
NVIDIA GRID 2.0 software is much more than a “driver”. While the software package does include a guest driver for Windows and Linux, it also includes the NVIDIA GRID vGPU manager for VMware vSphere and Citrix XenServer, as well as the license server and M6/M60 mode switching utility.
NVIDIA Tesla M6 and M60 profiles are specific to the M6 and M60. There will be similar profiles as to what NVIDIA had on K1 and K2 (512 MB through 4 GB), all with twice the number of users on M6/ M60 compared to K1/K2. Plus, there is an additional 8 GB profile on M6/M60 which also adds support for CUDA, which wasn’t available on K1/K2.
NVIDIA GRID 2.0 is Maxwell only. If you are an existing customer K1/K2 are unchanged and will remain as a parallel option.
GA of NVIDIA GRID 2.0 (M60 and M6) will be 15 September 2015.
To get NVIDIA GRID 2.0 if you are a Citrix customer you need:
Server hardware that supports NVIDIA GRID 2.0 +NVIDIA GPU M60 or M6 + NVIDIA vGPU Software license + Citrix XenDesktop or XenApp License (XenServer is included in XD/XA licenses)
To get NVIDIA GRID 2.0 if you are a VMware customer you need:
Server hardware that supports NVIDIA GRID 2.0 +NVIDIA GPU M60 or M6 + NVIDIA vGPU Software license + VMware Horizon license (Horizon includes vSphere for Desktop)
If you are a Citrix customer that wants to run on VMware vSphere you need:
Server hardware that supports NVIDIA GRID 2.0 + NVIDIA GPU M60 or M6 + NVIDIA vGPU Software license + Citrix XenDesktop or XenApp License + VMware vSphere Enterprise Plus license or vSphere for Desktop license
NVIDIA have released a new GRID Virtual GPU Manager 346.68 for Citrix XenServer 6.5 and VMware vSphere 6.
NVIDIA have in this release also released Windows drivers for vGPU 348.27
The GRID Virtual GPU Manager 346.68 is not updated in this release, its only the Windows drivers for vGPU 348.27
If you have GRID Virtual GPU Manager 346.68 installed in either XenServer or VMware you only need to update your VMs.
The GRID vGPU Manager and Windows guest VM drivers must be installed together.
Older VM drivers will not function correctly with this release of GRID vGPU Manager. Similarly, older GRID vGPU Managers will not function correctly with this release of Windows guest drivers.
Over the last several years, many of us in the industry have discussed the need for community driven End User Computing podcasts focusing on virtualization topics for people designing, deploying, and using Citrix, Microsoft, VMware and surrounding†technologies. I am excited to share that this month, two new Podcasts are being launched! First, a warm congratulations to Jarian Gibson and Andy Morgan on the successful launch of their Podcast, Frontline Chatter. Here’s to many years of continued success! Next, allow me to introduce the End User Computing Podcast!
I will in this article brush up some of the exciting stuff that NVIDIA announced last week at their GPU conference. Some of the big news was that VMware DaaS supports NVIDIA GRID technology with vSGA & vDGA. NVIDIA GRID vGPU technology will be GA with VMware vSphere in 2015, this is great news that VMware and NVIDIA is working close together and there will be a beta available later this year, so customers can start evaluate. If they wanna use vGPU with NVIDIA GRID the only Hypervisor is Citrix XenServer.
Lets look at which new technologies NVIDIA CEO Jen-Hsun Huang unveiled 25th March 2014 at NVIDIA GTC.
Pascal is the new GPU family that will follow this year’s Maxwell GPUs.
Named for 17th century French mathematician Blaise Pascal, our next-generation family of GPUs will include three key new features: stacked DRAM, unified memory, and NVLink.
Pascal is due in 2016.
NVIDIA announced a new interconnect called NVLink which enables the next step in harnessing the full potential of the accelerator, and the Pascal GPU architecture with stacked memory, slated for 2016.
Pascal will support stacked memory, a technology which enables multiple layers of DRAM components to be integrated vertically on the package along with the GPU. Stacked memory provides several times greater bandwidth, more than twice the capacity, and quadrupled energy efficiency, compared to current off-package GDDR5. Stacked memory lets us combine large, high-bandwidth memory in the same package with the GPU, allowing us to place the place the voltage regulators close to the chip for efficient power delivery. Stacked Memory, combined with a new Pascal module that is one-third the size of current PCIe boards, will enable us to build denser solutions than ever before.
Today a typical system has one or more GPUs connected to a CPU using PCI Express. Even at the fastest PCIe 3.0 speeds (8 Giga-transfers per second per lane) and with the widest supported links (16 lanes) the bandwidth provided over this link pales in comparison to the bandwidth available between the CPU and its system memory. In a multi-GPU system, the problem is compounded if a PCIe switch is used. With a switch, the limited PCIe bandwidth to the CPU memory is shared between the GPUs. The resource contention gets even worse when peer-to-peer GPU traffic is factored in.
NVLink addresses this problem by providing a more energy-efficient, high-bandwidth path between the GPU and the CPU at data rates 5 to 12 times that of the current PCIe Gen3. NVLink will provide between 80 and 200 GB/s of bandwidth, allowing the GPU full-bandwidth access to the CPU’s memory system.
The basic building block for NVLink is a high-speed, 8-lane, differential, dual simplex bidirectional link. Our Pascal GPUs will support a number of these links, providing configuration flexibility. The links can be ganged together to form a single GPU↔CPU connection or used individually to create a network of GPU↔CPU and GPU↔GPU connections allowing for fast, efficient data sharing between the compute elements.
When connected to a CPU that does not support NVLink, the interconnect can be wholly devoted to peer GPU-to-GPU connections enabling previously unavailable opportunities for GPU clustering.
Moving data takes energy, which is why we are focusing on making NVLink a very energy efficient interconnect. NVLink is more than twice as efficient as a PCIe 3.0 connection, balancing connectivity and energy efficiency.
Understanding the value of the current ecosystem, in an NVLink-enabled system, CPU-initiated transactions such as control and configuration are still directed over a PCIe connection, while any GPU-initiated transactions use NVLink. This allows us to preserve the PCIe programming model while presenting a huge upside in connection bandwidth.
Today, developers devote a lot of effort to optimizing and avoiding PCIe transfer bottlenecks. Current applications that have devoted time to maximizing concurrency of computation and communication will enjoy a boost from the enhanced connection.
NVLink and stacked memory enable acceleration of a whole new class of applications. The large increase in GPU memory size and bandwidth provided by stacked memory will enable GPU applications to access a much larger working set of data at higher bandwidth, improving efficiency and computational throughput, and reducing the frequency of off-GPU transfers. Crafting and optimizing applications that can exploit the massive GPU memory bandwidth as well as the CPU↔GPU and GPU↔GPU bandwidth provided by NVLink will allow you to take the next steps towards exascale computing.
Starting with CUDA 6, Unified Memory simplifies memory management by giving you a single pointer to your data, and automatically migrating pages on access to the processor that needs them. On Pascal GPUs, Unified Memory and NVLink will provide the ultimate combination of simplicity and performance. The full-bandwidth access to the CPU’s memory system enabled by NVLink means that NVIDIA’s GPU can access data in the CPU’s memory at the same rate as the CPU can. With the GPU’s superior streaming ability, the GPU will sometimes be able to stream data out of the CPU’s memory system even faster than the CPU.
NVIDIA have released a new physically VCA appliance with the name VCA IRAY. Price will be only $ 50.000 for 1 VCA IRAY appliance and includes an Iray license and the first year of maintenance and updates. GA is Summer 2014.
The CTO of VMware was on stage with CEO of NVIDIA and talked about their new partnership how VMware will embrace NVIDIA for their DaaS strategy and their hypervisor ESX.
The Horizon DaaS solution is available today. Navisite will be first service provider to deliver this.
NVIDIA and VMware is working on with integrating NVIDIA vGPU with ESX/vSphere which will be available in Q3 2014 (BETA), and with general availability(FINAL) in 2015.