Archives for December 13, 2018

After another data leak, its second such leak in a year, Google today announced it was shutting down its beleaguered social media platform, Google+. API access will shut down even sooner, within the next 90 days.

The newest vulnerability affected 52.5 million users, according to Google. Profile information, including names, email addresses, age, and occupation were all exposed. Worse, accounts set to private were still affected. Apps may have also stolen data stolen with specific Google+ users, but not publicly.

“With the discovery of this new bug, we have decided to expedite the shut-down of all Google+ APIs; this will occur within the next 90 days,” says David Thacker, VP of project management at Google, in a blog post. “In addition, we have also decided to accelerate the sunsetting of consumer Google+ from August 2019 to April 2019.”

Thacker says Google discovered the bug as part of its standard testing procedure, stating that there is “no evidence” that developers who had access to this data were aware of it, or had misused it.

Google has already begun notifying users affected by the bug.

In October, a similar Google+ vulnerability may have exposed data to app developers for as long as three years. The bug was discovered in March, but not publicly disclosed until October.

This leak, Thacker says, was discovered on its own, and live for just six days.

Intel just announced their new Sunny Cove Architecture that comes with a lot of new bells and whistles. The Intel processor line-up has been based off the Skylake architecture since 2015, so the new architecture is a fresh breath for the world’s largest chip maker. They’ve been in the limelight this year with hardware vulnerabilities exposed, known as Spectre and Meltdown. The new designs have of course been patched against those weaknesses.

The new architecture (said to be part of the Ice Lake-U CPU) comes with a lot of new promises such as faster core, 5 allocation units and upgrades to the L1 and L2 caches. There is also support for the AVX-512 or Advanced Vector Extensions instructions set which will improve performance for neural networks and other vector arithmetic.

Another significant change is the support for 52-bits of physical space and 57 bits of linear address support. Today’s x64 CPUs can only use bit 0 to bit 47 for an address space spanning 256TB. The additional bits mean a bump to a whooping 4 PB of physical memory and 128 PB of virtual address space.

The new offering was demoed under the company’s 10nm process which incidentally is the same as the previously launched Cannon Lake. The new processors are due in the second half of 2019 and are being heavily marketed as a boon for the Cryptography and Artificial Intelligence Industries. The claim is that for AI, memory to CPU distance has been reduced for faster access, and that special cryptography-specific instructions have been added.

NVIDIA has set six AI performance records with today’s release of the industry’s first broad set of AI benchmarks.

Backed by Google, Intel, Baidu, NVIDIA and dozens more technology leaders, the new MLPerf benchmark suite measures a wide range of deep learning workloads. Aiming to serve as the industry’s first objective AI benchmark suite, it covers such areas as computer vision, language translation, personalized recommendations and reinforcement learning tasks.

NVIDIA achieved the best performance in the six MLPerf benchmark results it submitted for. These cover a variety of workloads and infrastructure scale – ranging from 16 GPUs on one node to up to 640 GPUs across 80 nodes.

The six categories include image classification, object instance segmentation, object detection, non-recurrent translation, recurrent translation and recommendation systems. NVIDIA did not submit results for the seventh category for reinforcement learning, which does not yet take advantage of GPU acceleration.

A key benchmark on which NVIDIA technology performed particularly well was language translation, training the Transformer neural network in just 6.2 minutes. More details on all six submissions are available on the NVIDIA Developer news center.

NVIDIA engineers achieved their results on NVIDIA DGX systems, including NVIDIA DGX-2, the world’s most powerful AI system, featuring 16 fully connected V100 Tensor Core GPUs.

NVIDIA is the only company to have entered as many as six benchmarks, demonstrating the versatility of V100 Tensor Core GPUs for the wide variety of AI workloads deployed today.

“The new MLPerf benchmarks demonstrate the unmatched performance and versatility of NVIDIA’s Tensor Core GPUs,” said Ian Buck, vice president and general manager of Accelerated Computing at NVIDIA. “Exceptionally affordable and available in every geography from every cloud service provider and every computer maker, our Tensor Core GPUs are helping developers around the world advance AI at every stage of development.”

State-of-the-Art AI Computing Requires Full Stack Innovation
Performance on complex and diverse computing workloads takes more than great chips. Accelerated computing is about more than an accelerator. It takes the full stack.

NVIDIA’s stack includes NVIDIA Tensor Cores, NVLink, NVSwitch, DGX systems, CUDA, cuDNN, NCCL, optimized deep learning framework containers and NVIDIA software development kits.

NVIDIA’s AI platform is also the most accessible and affordable. Tensor Core GPUs are available on every cloud and from every computer maker and in every geography.

The same power of Tensor Core GPUs is also available on the desktop, with the most powerful desktop GPU, NVIDIA TITAN RTX costing only $2,500. When amortized over three years, this translates to just a few cents per hour.

And the software acceleration stacks are always updated on the NVIDIA GPU Cloud (NGC) cloud registry.

NVIDIA’s Record-Setting Platform Available Now on NGC
The software innovations and optimizations used to achieve NVIDIA’s industry-leading MLPerf performance are available free of charge in our latest NGC deep learning containers. Download them from the NGC container registry.

The containers include the complete software stack and the top AI frameworks, optimized by NVIDIA. Our 18.11 release of the NGC deep learning containers includes the exact software used to achieve our MLPerf results.

Developers can use them everywhere, at every stage of development:

• For data scientists on desktops, the containers enable cutting-edge research with NVIDIA TITAN RTX GPUs.
• For workgroups, the same containers run on NVIDIA DGX Station.
• For enterprises, the containers accelerate the application of AI to their data in the cloud with NVIDIA GPU-accelerated instances from Alibaba Cloud, AWS, Baidu Cloud, Google Cloud Platform, IBM Cloud, Microsoft Azure, Oracle Cloud Infrastructure and Tencent Cloud.
• For organizations building on-premise AI infrastructure, NVIDIA DGX systems and NGC-Ready systems from Atos, Cisco, Cray, Dell EMC, HP, HPE, Inspur, Lenovo, Sugon and Supermicro put AI to work.

Last year at CES 2018, Samsung unveiled the Note 9 Pen, a lightweight 13.3-inch convertible aimed at artists and anyone else who needed decent power with as little weight as possible. The model is back again in a big way, with an all-new design and features that should fix what we didn’t like about it before. Most notably, it comes in both 13-inch and 15-inch versions, has more ports and packs a much bigger battery with fast-charging that lets it run for up to 15 hours.

The 9 Pen is still powered by an 8th-generation Intel Core i7 processor, but you can get the 15-inch version with 2GB NVIDIA GeForce MX150 discrete graphics. (The smaller model is limited to integrated Intel graphics.) Both have LPDDR3 RAM, GIGA WiFi, face and fingerprint recognition, two Thunderbolt 3 ports, USB-C, headphone/microphone and UFS/MicroSD ports. It’s also got a “ThunderAmp” smart amp and “studio-quality sound from speakers tuned by AKG,” Samsung said.

The 2019 Note 9 Pen also looks less cheap than the last model, thanks to a new design and all-metal/aluminum frame. Both models are still incredibly light, at 2.47 pounds for the 13-inch version, and 3.44 pounds for the larger model. The display, with 1080p resolution as before, has got slimmed-down bezels, at least on the sides.

Finally, both models have a built-in S Pen with a choice of three different tips and half the latency of the last mode. Combined with the lightweight convertible design, drawing app support and discrete graphics, that should make it a decent laptop for artists and graphics pros on the go. Samsung hasn’t announced pricing, but the last Notebook 9 Pen started at $1,400, if that helps. It’ll arrive in the US sometime in 2019.