Nor-Tech Introduces Portable HPC Clusters for the Oil & Gas Industry

Nor-Tech Introduces Portable HPC Clusters for the Oil & Gas Industry

By Unknown   03:51   No comments:

Oct. 11 — Nor-Tech (Northern Computer Technologies), the leading provider of HPC clusters and high performance workstations, just introduced the first portable HPC cluster for the oil and gas industry with a price point starting at $20,000. 

Nor-Tech designed the cluster with the understanding that the oil and gas industry is looking for providers:

• With deep FEA/CFD/CAE experience and an engineering staff that understands the concepts and challenges.
• With knowledge of all software integration options and assurance of agnostic recommendations.
• That have the willingness to listen to clients, the ability to understand their needs, and the ability to incorporate those needs into the design.
• That can deliver a superior cost/performance ratio and a competitive total cost of ownership.
• That have an excellent reputation within the HPC community, and
• That offer a strong product lineup backed by quality service and prompt support.

Nor-Tech President and CEO David Bollig said, “Many businesses in the oil and gas industry in particular have been putting off upgrading from a workstation to a cluster even though they know it will dramatically reduce modeling time. Part of the reason is the price and the other part is the anticipated disruption. We’ve been working with clients in just this situation for many years and have perfected the transition process. Our goal is to have clients up and running with their new cluster within a couple hours of delivery. We also back that up with no wait time support.”

Nor-Tech’s cluster was developed with dual goals of mitigating risk and maximizing the speed of obtaining accurate, actionable results at a price within reach of even the smallest oil and gas companies. The cluster aggregates, processes, and interprets a terabyte or more of data originating in simulations, surveys, historical geological sources, etc.

Often, as organizations need more processing power than a single workstation can provide, they will add another workstation. It seems like a cost-saving strategy at first, but as they continue to add workstations, they soon exceed the price of a single cluster—which would also have about 3x the processing power.

“We always recommend the most cost-effective solution for the long term,” Bollig said. “So we do offer high end workstations that may make more sense for start-ups still struggling with desktops.”

About Nor-Tech

2016 HPCwire award finalist, Nor-Tech is renowned throughout the scientific, academic, and business communities for easy to deploy turnkey clusters and expert, no wait time support. All of Nor-Tech’s technology is made by Nor-Tech in Minnesota and supported by Nor-Tech around the world. In addition to HPC clusters, Nor-Tech’s custom technology includes workstations, desktops, and servers for a range of applications including CAE, CFD, and FEA. Nor-Tech engineers average 20+ years of experience and are responsible for significant high performance computing innovations. The company has been in business since 1998 and is headquartered in Burnsville, Minn. just outside of Minneapolis. To contact Nor-Tech call 952-808-1000/toll free: 877-808-1010 or visit http://www.nor-tech.com. For more information on Nor-Tech’s clusters visit: http://www.nor-tech.com/solutions/hpc.

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Intel's new PC, IoT chief brings fresh ideas to the veteran chip maker

Intel's new PC, IoT chief brings fresh ideas to the veteran chip maker

By Unknown   23:53   No comments:

Intel's second-in-command Venkata Renduchintala is feeling at home with his new company after he switched over from Qualcomm

 Venkata Renduchintala is president of Intel's Client and Internet of Things (IoT) businesses and Systems Architecture Group.

Intel is now more than just a PC company. At industry events, the company's keynotes feature drones flying around, robots walking on stage and musicians creating tunes from wearables. The chip maker is helping BMW build an autonomous car, will sell modems to Apple, and is leading the development of next-generation 5G cellular networks. For all these new markets, it will provide chip and data-center technologies.

The transformation is happening partly under the leadership of Venkata Renduchintala, president of the Client and Internet of Things (IoT) Businesses and Systems Architecture Group at Intel. As Intel's second-in-command, he helped cut struggling products like mobile CPUs and sharpened the company's focus on IoT, servers, and connectivity.

Hired from rival Qualcomm late last year, he's an outsider trying to rid Intel of its historical resistance to change. He's also bringing fresh ideas and wholesale changes to  Intel, which promises to bring a new dynamic to the Silicon Valley institution.

IDG News Service spoke with him on a range of topics including VR headsets, IoT, autonomous cars, competitors and the decision to cut products. This is an edited version of the discussion.

IDGNS: How have you settled into your new job? What drew you to Intel?

It's been a really interesting process of acclimation. It's a great mixture of feeling, like an organization where I think my experience and my interests can really help the journey [CEO] Brian [Krzanich] wants to undertake with the company. The scale at which Intel can play is probably going to be very difficult for others to match if you look across, client, networking and the data center groups. The goal is to be able to think as one Intel.

IDGNS: There have been questions on how you would fit into Intel, which has a closed culture and history of promoting executives internally. Many people hired from external companies haven't worked out.

One thing that's really important to understand is that Intel is a company of tremendous heritage. I'm not coming in to fix anything. I'm coming in hopefully to add another dimension and an important ingredient to the management team that Brian has at his disposal. It requires me to respect what Intel has been able to achieve and the caliber of the management team and the brands assimilated. I don't think Brian hired me to maintain the status quo. I think what he wanted was a strong ingredient of outside-in thinking complementing the original thoughts. I'm feeling very comfortable now in being able to feel like I've got a good bunch of colleagues who know where I'm coming from; we can speak straight to each other and we can actually have really good discussions of meritocracy.

IDGNS: You had to make some decisions on cutting products Intel has worked on for years as the company's priorities were reset. How tough was it?

When you come into a company you have a degree of objectivity that isn't tainted by your attachments to the genesis of certain projects. For me it was a fairly structured, objective discussion where you make decisions in a transparent and open manner. As long as you can walk people through your thinking, you can take what was very controversial and make it very logical. I'm passionate about technology but I'm also passionate about profitability and how the two are married in a seamlessly reinforcing way.

IDGNS: What's the reasoning behind cutting mobile processors to focus on modems?

First of all, we rationalized what we were spending our R&D on. We had a couple of mobile SoC products that I don't think were worthy to continue to conclusion. That doesn't mean to say we're no longer doing mobile platforms. On the mobile platform side, my commitment is to talk less and do more. When we have something to say we'll talk about it.

On the modem side, it's a fundamental technology and this is where I think it comes down to being as indelible for us as our competence in CPU or GPU. We've set ourselves up with a very interesting road-map, but more importantly, we've established a degree of credibility, relevance and importance as a key technology partner with a number of key players in the industry that I think is really important.

IDGNS: What are your top priorities and goals?

I have three uber-level goals. One is to continue to drive our client computing business to a position of stable profitability in the face of a slowly declining [market]. I think we're doing well in that area. The second is to grow and scale our IoT business from something that's very interesting to something that's really substantial in the longer term. The IoT business for us is a microcosm of the entire company coming together -- we're creating a type of all-for-one, one-for-all mentality. The third is to maintain a degree of vibrancy in the technology leadership of our entire systems architecture organization. It's developing all the core technologies that really moves the competitive needle forward.

IDGNS: Intel's untethered mixed-reality headset called Project Alloy was big news at IDF. What are the expectations from Alloy and how are things going?

The whole point of having tetherless VR is a big deal. Everything we're doing in Alloy we're going to open-source. We can take VR and evolve it from the very rudimentary definitions today of [VR] in a smart phone that you clip into some kind of visor. You can move it to a capable, embedded PC that's driving two to three teraflops of computing and generate a really immersive experience. That was really it -- taking ideas out from the lab, productizing them, solving all those problems of integration, figuring out how RealSense and depth camera fits into all of that, figuring out how to do merged reality,  and saying "now go scale the ecosystem."

IDGNS: Is the VR headset the new PC?

I think it's another very interesting growth opportunity for the PC. I think it can generate a specific class of products in its own right. It will generate different segmentation points and probably a custom piece of silicon built on the PC platform that amplify the use case. So we're very excited about the whole VR space.

IDGNS: Intel hasn't given up on Moore's Law, though many believe it is reaching its end. How is Intel preparing for a future when manufacturing reaches atomic scale, and how will chips look beyond Kaby Lake?

Nobody inside Intel is coming anywhere near the kind-of-like fatalistic conclusions about where Moore's Law is. Intel has had a stellar track record in delivering node generation like clockwork. Maybe we've moved from a two-year to a two-and-a-half-year cadence, but we already see light at the end of the tunnel. We will continue to drive process technology and nobody is calling timeout on anything. We're working hard on 7-nanometer, we're talking about pathfinding for 5-nanometer. All of that is in the throes. We made a great announcement on Kaby Lake -- that's using an evolution of 14-nanometer transistor geometry that gave a substantially improved user experience compared to Skylake. We're going to continue to do more of that as we continue to drive process leadership.

IDGNS: Are you happy with your current chip line-up -- Kaby Lake for PCs, and Atom for IoT?

We have a competent portfolio of products. I'm in no way shape or form concluding they are complete and aren't going to be benefited from augmentation. For me I think it's really wanting to understand the use cases a lot more. I don't see an IoT strategy for Intel being one where everything is delivered by Intel. It's integrating a number of different technologies that could be indigenous to Intel, or could be created by other companies, but managed in a way where people could look at Intel as somebody providing the overarching framework of integration.

IDGNS: IoT is a big part of Intel's future. What's the strategy for that market?

That's a significant business. I think we're just starting. As you see the advent of autonomous driving vehicles, you see robots and drones start to ship in scale: those are very high value opportunities for us. We characterize our IoT interests into three verticals: industrial, transportation and retail -- all of them have an end-to-end dimension where we're providing a client environment, the networking infrastructure and the data analytics platform that drives all of that through industry partnerships.

IDGNS: Would in any way the ARM foundry deal help Intel achieve its goals in IoT and other areas? Would you be open to the idea of taking an ARM CPU license, as an example?

Open to? Yes. My view is fairly straightforward -- that Intel's IoT plan has to not only be able to harmoniously integrate Intel-based microprocessors and MCUs, it has to be able to aggregate and harmoniously integrate a plethora of different types of MCUs, whether it be ARM-based, MIPS-based, or proprietary MCUs. All of them have the ability to monitor, sense data that they want to get on to an information highway of some kind. Our ability to [support] many different client environments is going to be a necessity in any vertical IoT strategy we have. There are many areas in the ARM ecosystem where Intel can pragmatically play in for its own benefit. I'm a big believer in paying respect to established ecosystems.

IDGNS: Self-driving cars are a big deal for Intel. Could you talk about projects in the pipeline?

Our goal is to provide the type of computing power that dwarfs anything that exists in a car today, but basically make it mainstream. What we're doing on our Xeon Phi processor for machine learning and deep learning, what we're doing in computer vision and also supplemented by radar and lidar. Being able to aggregate that data, generate intelligence, make decisions on it with assistance from machine and deep learning algorithms -- that's all happening as we speak.

IDGNS: How do you see the autonomous car market evolving?

I see the first explosive area to be in the urban transportation environment where  services like Uber and Lyft will evolve and develop. There's going to be a lot of experimentation and path-finding to do in addition to technology creation. We're probably talking about a decade away. Stamina to invest is going to be really important;  those that have the stamina to stay the course are going to win big.

IDGNS: Nvidia is approaching the automotive markets aggressively with its GPUs, how will you compete?

I have a great deal of respect for Nvidia. But every time I think of Nvidia, I think about Californian wine where they can make great wine but it contains only one grape -- great Cabernet Sauvignon or a great Chardonnay. I love French wines and French wines are blends where you need to be great at growing Cabernet, great at growing Merlot, great at growing Cabernet Franc. The art is in the mixture. That's the benefit Intel has. We have GPU, we have CPU, we have custom silicon, we have embedded storage, we have FPGA. Nvidia's going to basically say "I've got GPUs and I've got GPUs and I've got GPUs." Great strategy, but it doesn't give anywhere near the extensibility, flexibility and scalability that Intel is able to offer.

IDGNS: How will 5G influence changes in the way devices are made and work?

5G is as much about the transformation of the network and the infrastructure as it is the client environment. [There is] going to be an even greater demand from mobile broadband bandwidth, people are going to want tens of gigabytes per second, if not hundreds of gigabytes per second. We're going to see much greater pervasiveness of client devices. If you talk about autonomous vehicles or delivering health services over a mobile network, you need to be able to make life or death decisions based on that. The network has to transform and the data center becomes a much higher order entity that's focused on massive data analytics that orchestrates that entire network.

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IBM and NVIDIA Move to Corner the Enterprise Market for AI

IBM and NVIDIA Move to Corner the Enterprise Market for AI

By Unknown   23:13   No comments:

A number of coming technologies will undoubtedly change the world as we know it. Two came to light last week while I was trying, and failing, to enjoy an infrequent vacation. One is a power storage technology that has high capacity and doesn’t catch fire or explode like lithium ion batteries. The other, and far more important, is artificial intelligence (AI), which has the potential to change our lives for the better or worse, but dramatically either way.

An alliance between two of the most powerful companies in this race, IBM and NVIDIA, was announced last week around a small, intelligent, rack-mounted server called the Power System S822LC. This was part of a three-server launch last week and I think the implications are really interesting. NVIDIA is naturally very excited about this.

Let’s explore why this partnership between two powerhouses could be really interesting.

Think

The word that IBM has connected with itself for much of my life is “Think,” and when it announced Watson, it put itself on a path to make that connection a reality. But Watson, as powerful as it is, is an intellectual baby when it comes to where the industry wants to go. Intelligent machines -- computers that can learn, adapt, and then make decisions based on data -- represent the future of computing and, some argue, the future of the human race.

TheChallenges of Cloud Integration - Download Now

This makes for an impressive potential world impact and the firm, or firms, that get this right first will likely own the next age of computing. IBM, with Watson, got the initial lead, but Watson is expensive to buy and expensive to train.

That’s why this isn’t a one-company effort. It can’t be; it will require a team.

NVIDIA 

Now, while IBM was working on large-scale AI, NVIDIA has been working on packaged intelligence as a technology. Its Drive PX, CX, and DGX-1 platforms are designed to make cars intelligent. However, DGX-1 goes well beyond this in that it forms the basis for the learning that other platforms can use in production. In short, you train the DGX-1 and it trains, at scale, everything else it feeds. This is close, in concept, to being able to manufacture things (initially cars) that come off the line with all of the knowledge they need to operate. If we were talking people, this would be like having a kid that starts out at birth knowing everything you know.

Now we just need to put the parts together.

IBM + NVIDIA

If we combine the two companies, we get the potential for not only a system that is far less expensive to buy but one that is far less expensive to train. The result may potentially be a system that is far smarter than Watson, far more capable than the DGX-1, and able to move both companies to the next tier.

OpenPOWER

The market is currently largely x86, and Intel dominates. Only one non-x86 platform has the potential to address this AI opportunity near term, and that is OpenPOWER, largely because it is backed by IBM and, unlike ARM, it is in production for servers of this class. It is also a technology shared by a variety of vendors, making it more attractive to customers like Google, which is aggressive with AI and particularly favors open systems.

When you combine IBM, NVIDIA and OpenPOWER, you get something unique and potentially very powerful in this race to intelligent computing.

Wrapping Up: Power of the Partnership

In the end, the eventual success of this effort will likely be directly attributable to how well IBM and NVIDIA partner over time. A similar partnership between IBM, Intel and Microsoft created the PC market. If IBM and NVIDIA can do better (that earlier partnership fell apart), then the potential for both firms to own this next technology wave is unmatched. If not, then we’ll just have another story about big firms failing to meet their potential.

For now, IBM and NVIDIA have the inside track, but it’s early in the race. While this new line of servers is a great start, as both companies know, it matters far less who leads a race at the beginning than who leads a race at the end.

Rob Enderle is President and Principal Analyst of the Enderle Group, a forward-looking emerging technology advisory firm.  With over 30 years’ experience in emerging technologies, he has provided regional and global companies with guidance in how to better target customer needs; create new business opportunities; anticipate technology changes; select vendors and products; and present their products in the best possible light. Rob covers the technology industry broadly. Before founding the Enderle Group, Rob was the Senior Research Fellow for Forrester Research and the Giga Information Group, and held senior positions at IBM and ROLM. Follow Rob on Twitter @enderle, on Facebook and on Google+.

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ARM Unveils Scalable Vector Extension for HPC at Hot Chips

ARM Unveils Scalable Vector Extension for HPC at Hot Chips

By Unknown   05:54   No comments:

ARM and Fujitsu today announced a scalable vector extension (SVE) to the ARMv8-A architecture intended to enhance ARM capabilities in HPC workloads. Fujitsu is the lead silicon partner in the effort (so far) and will use ARM with SVE technology in its post K computer, Japan’s next flagship supercomputer planned for the 2020 timeframe. This is an important incremental step for ARM, which seeks to push more aggressively into mainstream and HPC server markets.

Fujitsu first announced plans to adopt ARM for the post K machine – a switch from SPARC processor technology used in the K computer – at ISC2016 and said at the time that it would reveal more at Hot Chips about the ARM development effort needed. Bull Atos is also developing an ARM-based supercomputer.

The SVE is focused on addressing “next generation high performance computing challenges and by that we mean workloads typically found in scientific computing environment where they are very parallelizable,” said Ian Smythe, director of marketing programs, ARM Compute Products Group, in a pre-briefing. SVE is scalable from 128-bits to 2048-bits in 128-bit increments and, among other things, should enhance ARM’s ability to exploit fine grain parallelism.

Nigel Stephens, lead ISA architect and ARM Fellow, provided more technical detail in his blog (Technology Update: The Scalable Vector Extension (SVE) for the ARMv8-A Architecture, link below) coinciding with his Hot Chips presentation. It’s worth reading for a fast but substantial summary.

“Rather than specifying a specific vector length, SVE allows CPU designers to choose the most appropriate vector length for their application and market, from 128 bits up to 2048 bits per vector register,” wrote Stephens. “SVE also supports a vector-length agnostic (VLA) programming model that can adapt to the available vector length. Adoption of the VLA paradigm allows you to compile or hand-code your program for SVE once, and then run it at different implementation performance points, while avoiding the need to recompile or rewrite it when longer vectors appear in the future. This reduces deployment costs over the lifetime of the architecture; a program just works and executes wider and faster.

“Scientific workloads, mentioned earlier, have traditionally been carefully written to exploit as much data-level parallelism as possible with careful use of OpenMP pragmas and other source code annotations. It’s therefore relatively straightforward for a compiler to vectorize such code and make good use of a wider vector unit. Supercomputers are also built with the wide, high- bandwidth memory systems necessary to feed a longer vector unit,” wrote Stephens.

He notes that scientific workloads have traditionally been written to exploit as much data-level parallelism as possible with careful use of OpenMP pragmas and other source code annotations. “It’s relatively straightforward for a compiler to vectorize such code and make good use of a wider vector unit. Supercomputers are also built with the wide, high- bandwidth memory systems necessary to feed a longer vector unit.”

While HPC is a natural fit for SVE’s longer vectors, said Stephens, it also offers an opportunity to improve vectorizing compilers that will be of general benefit over the longer term as other systems scale to support increased data level parallelism.

Amplifying on the point, he wrote, “It is worth noting at this point that Amdahl’s Law tells us that the theoretical limit of a task’s speedup is governed by the amount of unparallelizable code. If you succeed in vectorizing 10 percent of your execution and make that code run four times faster (e.g. a 256-bit vector allows 4x64b parallel operations), then you reduce 1000 cycles down to 925 cycles and provide a limited speedup for the power and area cost of the extra gates. Even if you could vectorize 50 percent of your execution infinitely (unlikely!) you’ve still only doubled the overall performance. You need to be able to vectorize much more of your program to realize the potential gains from longer vectors.”

The ARMv7 Advanced SIMD (aka the ARM NEON) is now about 12 years old and was originally intended to accelerate media processing tasks on the main processor. With the move to AArch64, NEON gained full IEEE double-precision float, 64-bit integer operations, and grew the register file to thirty-two 128-bit vector registers. These changes, says Stephens, made NEON a better compiler target for general-purpose compute. SVE is a complementary extension that does not replace NEON, and was developed specifically for vectorization of HPC scientific workloads, he says.

Snapshot of new SVE features compared to NEON:

• Scalable vector length (VL)
• VL agnostic (VLA) programming
• Gather-load & Scatter-store
• Per-lane predication
• Predicate-driven loop control and management
• Vector partitioning and SW managed speculation
• Extended integer and floating- point horizontal reductions
• Scalarized intra-vector sub-loops

Smythe emphasized, “If you compile the code for SVE it will run on any implementation of SVE regardless of the width, whether 128 or 1024 or 2048, and the hardware implementation, that code will run on ARM architecture as a binary. That’s important and gives us scalability and compatibility into the future for the compilers and the code that HPC guys are writing.”
  
ARM has been steadily working to expand its ecosystem (shown here) with hopes of capturing a chunk of the broader x86 market. It has notable wins in many market segments, although the market traction has been tougher to gauge, and it is only in the past couple of years that server chips started to become available. Many design wins have been niche oriented; one example is an HPE ARM-based storage server (StoreVirtual 3200) announced earlier this month. ARM, of course, is a juggernaut in mobile computing.

Prior to the Hot Chips conference, with its distinctly technical focus, ARM was pre-briefing some of the HPC community about SVE and using the opportunity to reinforce its mission of growth, its success in ecosystem building, and to bask in some of the glory of the post K computer win. Given the recent acquisition of ARM by SoftBank, it will be interesting to watch how the marketing and technical activities change, if at all.

Lakshmi Mandyam, senior marketing director, ARM Server Programs, said, “We’ve been focusing on enabling some base market segments to establish some beachheads and enable our partners to get adoption in those key areas. Also we have also been using key end users to drive our approach in terms of ecosystem enablement because clearly we are catching up with x86 in terms of software enablement.”

“The move to open source and consuming applications and workloads through [as-a-service models] is really driving a lot of disruption of the industry. It also presents an opportunity because a lot of those platforms are based on open source and Linux and or intermediate middleware and so the dependency on the legacy (x86) software and architectures is gone. That presents an opportunity to ARM.”

It’s also important, she said, to recognize that many modern workloads, even in HPC, are moving towards the scale out model as opposed to a purely scale up. Many of those applications are driven by IO and memory performance. “This where the ARM partnership can shine because we are able to deliver heterogeneous computing quite easily and we’re able to deliver optimized algorithm processing quite easily. If you look at a lot of these applications, it’s not about spec and benchmark performance; it’s about what can you deliver in my application.”

“When you think about Fujitsu, as they talked about the post K computer, a lot of the folks are looking for this really tuned performance, to take a codesign approach where they are looking at the entire problem, and to deliver an application and service for a given problem. This is where their ability to tune platforms down to the silicon level pays big dividends,” she said.

Here’s a link to Nigel Stephens’ blog on the ARM SVE anouncment (Technology Update: The Scalable Vector Extension (SVE) for the ARMv8-A Architecture): https://community.arm.com/groups/processors/blog/2016/08/22/technology-update-the-scalable-vector-extension-sve-for-the-armv8-a-architecture

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ARM enters the supercomputer race

ARM enters the supercomputer race

By Unknown   04:20   No comments:

ARM’s newsroom site says nothing about it – yet. However, according to PC World, ARM’s first supercomputer chip will find its way into a machine based in Japan. The Post-K computer, to be developed by Fujitsu, and should be 50 – 100 times faster than its predecessor, the K Computer.

At peak performance, the K Computer delivers 10.5 petaflops (one quadrillion floating point operations per second (FLOPS). PC World says the new processor will be based on the 64-bit ARM-v8A architecture. It will have vector processing extensions called Scalabe Vector Extension.

ARM has made a name for itself creating mobile chips, and with its products being featured in Apple’s iPhone, it is a pretty powerful company. However, it was acquired by Japanese company Softbank, for $32 billion (£24.5bn). With this cash, ARM will be looking to strengthen its position in both servers and IoT (internet of things) industries.

What we should expect in the near(er) future is for these supercomputers to reach one exaflop. All hotshots (Intel, Nvidia, IBM), have been pushing to reach that goal some time now. Some media are also saying that ARM’s chips could be a more power-efficient alternative, knowing that large-scale supercomputers draw megawatts of power.

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New Cavium ThunderX2 adopts 64-bit ARM-based servers to address application and workload requirements

New Cavium ThunderX2 adopts 64-bit ARM-based servers to address application and workload requirements

By Unknown   02:34   No comments:

Semiconductor vendor Cavium announced Monday ThunderX2, its second generation of workload optimized ARM server SoCs that targets high performance volume servers deployed by public/private cloud and telecom communications data centers and high performance computing applications. It is optimized for data center workloads such as compute, security, storage, data analytics, network function virtualization and distributed databases.

The ThunderX2 line of processors currently includes four workload optimized processors targeting different workloads.

The ThunderX2_CP has been optimized for cloud compute workloads such as private and public clouds, web serving, web caching, web search, commercial HPC workloads such as computational fluid dynamics (CFD) and reservoir modeling. This line supports multiple 10/25/40/50/100 GbE network Interfaces and PCIe Gen3 interfaces. It also includes accelerators for virtualization and vSwitch offload.

The ThunderX2_ST has been optimized for big data, cloud storage, massively parallel processing (MPP) databases and Data warehousing workloads. This family supports multiple 10/25/40/50/100 GbE network interfaces, PCIe Gen3 interfaces and SATAv3 interfaces. It also includes hardware accelerators for data protection/ integrity/security, user to user efficient data movement.

The ThunderX2_SC has been optimized for secure web front-end, security appliances and cloud RAN type workloads. This family supports multiple 10/25/40/50/100 GbE interfaces and PCIe Gen3 interfaces. Integrated hardware accelerators include Cavium’s industry leading, 5th generation NITROX security technology with acceleration for IPSec, RSA and SSL.

The ThunderX2_NT has been optimized for media servers, scale-out embedded applications and NFV type workloads. This family supports multiple 10/25/40/50/100 GbE interfaces. It also includes OCTEON style hardware accelerators for packet parsing, shaping, lookup, QoS and forwarding.

“The Cavium ThunderX2 will expand the market opportunity for ARM-based server technologies by addressing demanding application and workload requirements for compute, storage networking and security,” said Simon Segars, CEO, ARM. “ThunderX2 demonstrates Cavium’s ability to deliver a combination of innovation and engineering execution and the new product family increases the momentum for server deployments powered by ARM processors in large scale data centers and end user environments.”

Cavium’s ThunderX2 SoC line is supported by a comprehensive software ecosystem ranging from platform level systems management and firmware to commercial operating systems, development environments and applications.

Cavium has actively engaged in server industry standards groups such as UEFI and delivered numerous reference platforms to an array of community and corporate partners. Cavium has also demonstrated its position in the open source software community driving upstream kernel enablement for ThunderX, actively contributing to Linaro’s enterprise and networking groups, investing in Linux Foundation projects such as Xen and OPNFV and sponsoring the FreeBSD Foundation’s ARMv8 server implementation.

ThunderX2 will deliver two to three times the performance across a range of standard benchmarks and applications compared to ThunderX, while boosting the market reach of the ThunderX line of processors by targeting applications that require high single thread performance such as web search, graph analytics, a variety of enterprise applications such as massively parallel processing (MPP) databases, data warehousing and enterprise HPC applications such as computational fluid dynamics (CFD) and reservoir modelling. ThunderX2 will deliver comparable performance at a better total cost of ownership compared to the next generation of traditional server processors.

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Cavium ThunderX Benchmarks Part II: Why enterprise ARM developers need these machines

Cavium ThunderX Benchmarks Part II: Why enterprise ARM developers need these machines

By Unknown   00:06   No comments:

Several months ago we had pre-release benchmarks of the Cavium ThunderX. The company promptly contacted us and wanted to show what its hardware can do. Those benchmarks were done on an older OS with older software. Over the past few weeks we have been working with both the single and dual socket (48 core and 96 core) variants of the Cavium ThunderX part and what struck us is how fast the software side is maturing in key areas. We will have more in-depth benchmarks of the platforms running real world workloads soon.

Prior to the release of the Cavium ThunderX most 64-bit ARM development, even for server applications, has been done on low price ARM development boards. There the typical core and memory count is both fixed and low. Networking is often provided by a USB to Ethernet adapter. This is a scene of typical ARM development hardware to date at many Silicon Valley startups:

While that is great for IoT development, the Cavium ThunderX platform is completely different. There are both single and dual processor configurations scaling up to 96 64-bit ARM cores. Memory capacities can scale into the TB range, or about 1000x a typical IoT development board. Networking provided on our test platforms is 80Gbps for our single processor system and 160Gbps on our dual processor system. Onboard storage can support more than a dozen SSDs or hard drives. Here is what our dual Cavium ThunderX 96 core test platform (a Gigabyte R270-T61) looks like inside:

The bottom line is, if you are developing for ARM in the data center, you need to get a Cavium ThunderX platform as it is the best data center ARM platform generally available today. In the remainder of this article, we are going to show how some benchmarks around the evolving software and development ecosystems. These benchmarks will show how the Cavium ThunderX is a competitive server platform. With a few weeks of working with the hardware/ software, and given the fact we manage both lab and production servers with Ubuntu, we are going to share some anecdotal experiences as well.

The Ubuntu 16.04 LTS Update

We originally received our single Cavium ThunderX 48 core system Gigabyte R120-T30 that we reviewed here. It had Ubuntu 14.04 LTS pre-installed from Cavium. After poking around with the machine running in our data center, there were a few nuances to the setup and ARM platforms:

• - Using Ubuntu 14.04 LTS required quite a bit of patching to get great performance
• - Trying to pull working software via “apt-get install” if it resided in universe did not always work. Sometimes packages were just not present. Those that did install were not optimized.
• - As Cavium pointed out, using newer gcc versions and building applications from the latest source was often the way to get good performance out of ARM platforms.

We updated the 1S ThunderX platform to Ubuntu 16.04 LTS the same week we received the 2S ThunderX platform in our data center. It was immediately clear that the experience was much better. Software that required patching instead worked out of the box. Packages installed from repos almost every time with even many multiverse packages working without having to custom compile software. This was a completely different experience.

The update had two implications. First, unlike Ubuntu 14.04 LTS, 16.04 LTS felt more like it just worked. Second, out of box performance was much better than in 14.04.

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