AMD Overtakes Nvidia in Graphics Shipments for First Time in 5 Years


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AMD saw its share of the graphics market surge in Q2 2019, with total shipments larger than Nvidia for the first time in five years. At the same time, Nvidia retains a hard lock on the add-in board market for desktops, with approximately two-thirds of total market share. And while these gains are significant, it’s also worth considering why they didn’t drive any particular “pop” in AMD’s overall financial figures for Q2.

First, let’s talk about the total graphics market. There are three players here: Intel, AMD, and Nvidia. Because this report considers the totality of the graphics space, and 2/3 of systems ship without a separate GPU,SEEAMAZON_ET_135 See Amazon ET commerce both AMD and Nvidia are minority players in this market. AMD, however, has an advantage — it builds CPUs with an onboard graphics solution, like Intel. Nvidia does not. Thus, we have to acknowledge that the total market space includes companies with a very different suite of products:

Intel: Integrated-only (until next year), no discrete GPUs, but accounts for a majority of total shipments.
AMD: Integrated GPUs and discrete cards, but with very little presence in upper-end mobile gaming.
Nvidia: No integrated solutions. Discrete GPUs only.

Graphics-Market-Share-JPR

According to JPR, AMD’s shipments increased by 9.8 percent, Intel shipments fell by 1.4 percent, and Nvidia shipments were flat, at 0.04 percent. This jives with reports from early in the year, which suggested that AMD would take market share from Intel due to CPU shortages. Separately from its global report, JPR also publishes a separate document on the desktop add-in board (AIB) market. This report only considers the discrete GPU space between Nvidia and AMD (Intel will compete in this space when it launches Xe next year). AMD and Nvidia split this space — and again, AMD showed significant growth, with a ten percent improvement in market share.

Image by Jon Peddie Research

If you pay attention to financial reports, however, you may recall that AMD’s Q2 2019 sales results were reasonable, but not spectacular. Both companies reported year-on-year sales declines. Nvidia’s fiscal year Q2 2020 results, which the company reported a few weeks back, showed gaming revenue falling 27 percent year-on-year. AMD doesn’t break out GPU and CPU sales — it combines them both into a single category — but its combined Compute and Graphics revenue reports were lower on a yearly basis as well:

AMD-Financial-Q2-2019

During the first half of the year, AMD was thought to be gaining market share at Intel’s expense, but these gains were largely thought to be at the low-end of the market. AMD launched its first Chromebooks with old Carrizo APUs, for example. This explains the growth in unit shipments in the total GPU space, as well as why the company didn’t show a tremendous profit from its gains. Growth in the AIB market may be explained by the sale of GPUs like the RX 570. This card has consistently been an incredibly good value — Nvidia didn’t bother distributing review GPUs for the GTX 1650 because the RX 570 is decisively faster, according to multiple reviews. But GPU sales have been down overall. According to JPR, AIB sales fell 16.6 percent quarter-to-quarter, and 39.7 percent year-on-year.

This explains why AMD’s strong market share gains didn’t translate to improved C&G sales revenue. The company earns less revenue on low-end sales compared with high-end cards. And its market share improvements have been overshadowed by a huge decline in AIB sales year-on-year, likely due to the combination of lingering crypto hangover and a weak overall enthusiast market in Q2.

Q3 will be a much more significant quarter for both companies. Not only does it typically improve on the basis of seasonality alone, but both Nvidia and AMD introduced price cuts and new products. AMD’s Navi powers the excellent 5700 and 5700 XT, which are both faster than the Nvidia refreshes of the RTX 2060 and RTX 2070 (now dubbed the RTX 2060 Super and RTX 2070 Super, respectively). Nvidia, in turn, offers ray tracing and variable rate shading — two features that are used in very few games today but may become more popular in the future. AMD lacks these features.

The two companies have staked out opposing strategies for boosting their respective market share. It’ll be interesting to see how consumers do or don’t respond to their separate value propositions.

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New 3DMark Benchmark Shows the Performance Impact of Variable Rate Shading


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One of the new features baked into DirectX 12 is support for variable-rate shading, also known as coarse-grained shading. The idea behind variable-rate shading is simple: In the vast majority of 3D games, the player doesn’t pay equal attention to everything on-screen. As far as the GPU is concerned, however, every pixel on-screen is typically shaded at the same rate. VRS / CGS allows the shader work being done for a single pixel to be scaled across larger groups of pixels; Intel demoed this feature during its Architecture Day last year, showing off a 2×2 as well as a 4×4 grid block.

In a blog post explaining the topic, Microsoft writes:

VRS allows developers to selectively reduce the shading rate in areas of the frame where it won’t affect visual quality, letting them gain extra performance in their games. This is really exciting, because extra perf means increased framerates and lower-spec’d hardware being able to run better games than ever before.

VRS also lets developers do the opposite: using an increased shading rate only in areas where it matters most, meaning even better visual quality in games.

VRS is a trick in a long line of tricks intended to help developers focus GPU horsepower where they need it most. It’s the sort of technique that’s going to become ever more important as Moore’s law slows down and it becomes harder and harder to wring more horsepower out of GPUsSEEAMAZON_ET_135 See Amazon ET commerce from process-node advances. 3DMark recently added a new benchmark to show the impact of VRS.

First, here’s a comparison of what the feature looks like enabled versus disabled.

VRS Disabled. Image provided by UL. Click to enlarge.

VRS Enabled. Image provided by UL. Click to enlarge.

There’s also a video of the effect in action, which gives you an idea of how it looks in motion.

As for the performance impact, Hot Hardware recently took the feature for a spin on Intel’s 10th Generation GPUs. Performance improvement from activating this feature was ~40 percent.

Data by Hot Hardware

These gains are not unique to Intel. HH also tested multiple Nvidia GPUs and saw strong gains for those cards as well. Unfortunately, VRS is currently confined to Nvidia and Intel-only — AMD does not support the capability and may not have the ability to activate it in current versions of Navi.

Elements in red receive full shading. Elements in green receive variable shading.

It always takes time to build support for features like this, so lacking an option at debut is not necessarily a critical problem. At the same time, however, features that save GPU rendering horsepower by reducing the impact of using various features tend to be popular among developers. It can help games run on lower-power solutions and in form factors that they might not otherwise support. All of rasterization is basically tricks to model what the real world looks like without actually having to render one, and choosing where to spend one’s resources to maximize performance is an efficiency boosting trick developers love. Right now, support is limited to a few architectures — Turing and Intel Gen 11 integrated — but that will change in time.

VRS isn’t currently used by any games, but Firaxis has demoed the effect in Civilization VI, implying that support might come to that title at some point. The new VRS benchmark is a free update to 3DMark Advanced or Professional Edition if you own those versions, but is not currently included in the free Basic edition.

The top image for this article is the VRS On screenshot provided by UL. Did you notice? Fun to check either way. 

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RTX 2080 vs. Radeon VII vs. 5700 XT: Rendering and Compute Performance


Most of our GPU coverage focuses on the consumer side of the business and on game benchmarking, but I promised to examine the compute side of performance back when the Radeon VII launched. With the 5700 XT having debuted recently, we had an opportunity to return to this question with a new GPU architecture from AMD and compare RDNA against GCN.

In fact, the overall compute situation is at an interesting crossroads. AMD has declared that it wishes to be a more serious player in enterprise compute environments but has also said that GCN will continue to exist alongside RDNA in this space. The Radeon VII is a consumer variant of AMD’s MI50 accelerator, with half-speed FP64 support. If you know you need double-precision FP64 compute, for example, the Radeon VII fills that niche in a way that no other GPU in this comparison does.

AMD-versus-Nvidia-Chart

The Radeon VII has the highest RAM bandwidth and it’s the only GPU in this comparison to offer much in the way of double-precision performance. But while these GPUs have relatively similar on-paper specs, there’s significant variance between them in terms of performance — and the numbers don’t always break the way you think they would.

One of AMD’s major talking points with the 5700 XTSEEAMAZON_ET_135 See Amazon ET commerce is now Navi represents a fundamentally new GPU architecture. The 5700 XT proved itself to be moderately faster than the Vega 64 in our testing on the consumer side of the equation, but we wanted to check the situation in compute as well. Keep in mind, however, that the 5700 XT’s newness also works against us a bit here. Some applications may need to be updated to take full advantage of its capabilities.

Regarding Blender 2.80

Our test results contain data from both Blender 2.80 and the standalone Blender benchmark, 1.0beta2 (released August 2018). Blender 2.80 is a major release for the application, and it contains a number of significant changes. The standalone benchmark is not compatible with Nvidia’s RTX family, which necessitated testing with the latest version of the software. Initially, we tested the Blender 2.80 beta, but then the final version dropped — so we dumped the beta results and retested.

Image by Blender

There are significant performance differences between the Blender 1.0beta2 benchmark and 2.80 and one scene, Classroom, does not render properly in the new version. This scene has been dropped from our 2.80 comparisons. Blender allows the user to specify a tile size in pixels to control how much of the scene is worked on at once. Code in the Blender 1.0beta2 benchmark’s Python files indicates that the test uses a tile size of 512×512 (X/Y coordinates) for GPUs and 16×16 for CPUs. Most of the scene files actually contained within the benchmark, however, actually use a tile size of 32×32 by default if loaded within Blender 2.80.

We tested Blender 2.80 in two different modes. First, we tested all compatible scenes using the default tile size those scenes loaded with. This was 16×16 for Barbershop_Interior, and 32×32 for all other scenes. Next, we tested the same renders with a default tile size of 512×512. Up until now, the rule with tile sizes has been that larger sizes were good for GPUs, while smaller sizes were good for CPUs. This appears to have changed somewhat with Blender 2.80. AMD and Nvidia GPUs show very different responses to larger tile sizes, with AMD GPUs accelerating with higher tile sizes and Nvidia GPUs losing performance.

Because the scene files we are testing were created in an older version of Blender, it’s possible that this might be impacting our overall results. We have worked extensively with AMD for several weeks to explore aspects of Blender performance on GCN GPUs. GCN, Pascal, Turing, and RDNA all show a different pattern of results when moving from 32×32 to 512×512, with Turing losing less performance than Pascal and RDNA gaining more performance in most circumstances than GCN.

All of our GPUs benefited substantially from not using a 16×16 tile size for Barbershop_Interior. While this test defaults to 16×16 it does not render very well at that tile size on any GPU.

Troubleshooting the different results we saw in the Blender 1.0Beta2 benchmark versus the Blender 2.80 beta and finally Blender 2.80 final has held up this review for several weeks and we’ve swapped through several AMD drivers while working on it. All of our Blender 2.80 results were, therefore, run using Adrenaline 2019 Edition 19.8.1.

Test Setup and Notes

All GPUs were tested on an Intel Core i7-8086K system using an Asus Prime Z370-A motherboard. The Vega 64, Radeon RX 5700 XT, and Radeon VII were all tested using Adrenalin 2019 Edition 19.7.2 (7/16/2019) for everything but Blender 2.80. All Blender 2.80 tests were run using 19.8.1, not 19.7.2. The Nvidia GeForce GTX 1080 and Gigabyte Aorus RTX 2080 were both tested using Nvidia’s 431.60 Game Ready Driver (7/23/2019).

CompuBench 2.0 runs GPUs through a series of tests intended to measure various aspects of their compute performance. Kishonti, developers of CompuBench, don’t appear to offer any significant breakdown on how they’ve designed their tests, however. Level set simulation may refer to using level sets for the analysis of surfaces and shapes. Catmull-Clark Subdivision is a technique used to create smooth surfaces. N-body simulations are simulations of dynamic particle systems under the influence of forces like gravity. TV-L1 optical flow is an implementation of an optical flow estimation method, used in computer vision.

SPEC Workstation 3.1 contains many of the same workloads as SPECViewPerf, but also has additional GPU compute workloads, which we’ll break out separately. A complete breakdown of the workstation test and its application suite can be found here. SPEC Workstation 3.1 was run in its 4K native test mode. While this test run was not submitted to SPEC for formal publication, our testing of SPEC Workstation 3.1 obeyed the organization’s stated rules for testing, which can be found here.

Nvidia GPUsSEEAMAZON_ET_135 See Amazon ET commerce were always tested with CUDA when CUDA was available.

We’ve cooked up two sets of results for you — a synthetic series of benchmarks, created with SiSoft Sandra and investigating various aspects of how these chips compare, including processing power, memory latency, and internal characteristics, and a wider suite of tests that touch on compute and rendering performance in various applications. Since the SiSoft Sandra 2020 tests are all unique to that application, we’ve opted to break them out into their own slideshow.

The Gigabyte Aorus RTX 2080 results should be read as approximately equivalent to an RTX 2070S. The two GPUs perform nearly identically in consumer workloads and should match each other in workstation as well.

SiSoft Sandra 2020

SiSoft Sandra is a general-purpose system information utility and full-featured performance evaluation suite. While it’s a synthetic test, it’s probably the most full-featured synthetic evaluation utility available, and Adrian Silasi, its developer, has spent decades refining and improving it, adding new features and tests as CPUs and GPUs evolve.

Our SiSoft Sandra-specific results are below. Some of our OpenCL results are a little odd where the 5700 XT is concerned, but according to Adrian, he’s not yet had the chance to optimize code for execution on the 5700 XT. Consider these results to be preliminary — interesting, but perhaps not yet indicative — as far as that GPU is concerned.

Our SiSoft Sandra 2020 benchmarks point largely in the same direction. If you need double-precision floating-point, the Radeon VII is a compute monster. While it’s not clear how many buyers fall into that category, there are certain places, like image processing and high-precision workloads, where the Radeon VII shines.

The RDNA-based Radeon 5700 XT does less to distinguish itself in these tests, but we’re also in contact with Silasi concerning the issues we ran into during testing. Improved support may change some of these results in months ahead.

Test Results

Now that we’ve addressed Sandra performance, let’s turn to the rest of our benchmark suite. Our other results are included in the slideshow below:

Conclusions

What do these results tell us? A lot of rather interesting things. First of all, RDNA is downright impressive. Keep in mind that we’ve tested this GPU in professional and compute-oriented applications, none of which have been updated or patched to run on it. There are clear signs that this has impacted our benchmark results, including some tests that either wouldn’t run or it ran slowly. Even so, the 5700 XT impresses.

Radeon VII impresses too, but in different ways than the 5700 XT. SiSoft Sandra 2020 shows the advantage this card can bring to double-precision workloads, where it offers far more performance than anything else on the market. AI and machine learning have become much more important of late, but if you’re working in an area where GPU double-precision is key, Radeon VII packs an awful lot of firepower. SiSoft Sandra does include tests that rely on D3D11 rather than OpenCL. But given that OpenCL is the chief competitor to CUDA, I opted to stick with it in all cases save for the memory latency tests, which globally showed lower latencies for all GPUs when D3D was used compared with OpenCL.

AMD has previously said that it intends to keep GCN in-market for compute, with Navi oriented towards the consumer market, but there’s no indication that the firm intends to continue evolving GCN on a separate trajectory from RDNA. The more likely meaning of this is that GCN won’t be replaced at the top of the compute market until Big Navi is ready at some point in 2020. Based on what we’ve seen, there’s a lot to be excited about on that front. There are already applications where RDNA is significantly faster than Radeon VII, despite the vast difference between the cards in terms of double-precision capability, RAM bandwidth, and memory capacity.

Blender 2.80 presents an interesting series of comparisons between RDNA, GCN, and CUDA. Using higher tile sizes has an enormous impact on GPU performance, but whether that difference is good or bad depends on which brand of GPU you use and which architectural family it belongs to. Pascal and Turing GPUs performed better with smaller tile sizes, while GCN GPUs performed better with larger ones. The 512×512 tile size was better in total for all GPUs, but only because it improved the total rendering time on Barbershop_Interior by more than it harmed the render time of every other scene for Turing and Pascal GPUs. The RTX 2080 was the fastest GPU in our Blender benchmarks, but the 5700 XT put up excellent performance results overall.

I do not want to make global pronouncements about Blender 2.80 settings; I am not a 3D rendering expert. These test results suggest that Blender performs better with larger tile settings on AMD GPUs but that smaller tile settings may produce better results for Nvidia GPUs. In the past, both AMD and Nvidia GPUs have benefited from larger tile sizes. This pattern could also be linked to the specific scenes in question, however. If you run Blender, I suggest experimenting with different scenes and tile sizes.

Ultimately, what these results suggest is that there’s more variation in GPU performance in some of these professional markets than we might expect for gaming. There are specific tests where the 5700 XT is markedly faster than the RTX 2080 or Radeon VII and other tests where it falls sharply behind them. OpenCL driver immaturity may account for some of this, but we see flashes of brilliance in these performance figures. The Radeon VII’s double-precision performance put it in a class of its own in certain respects, but the Radeon RX 5700 XT is a far less expensive and quieter card. Depending on what your target application is, AMD’s new $400 GPU might be the best choice on the market. In other scenarios, both the Radeon VII and the RTX 2080 make specific and particular claim to being the fastest card available.

Feature image is the final render of the Benchmark_Pavilion scene included in the Blender 1.02beta standalone benchmark. 

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Nvidia and VMware team up to make GPU virtualization easier – gpgmail


Nvidia today announced that it has been working with VMware to bring its virtual GPU technology (vGPU) to VMware’s vSphere and VMware Cloud on AWS. The company’s core vGPU technology isn’t new, but it now supports server virtualization to enable enterprises to run their hardware-accelerated AI and data science workloads in environments like VMware’s vSphere, using its new vComputeServer technology.

Traditionally (as far as that’s a thing in AI training), GPU-accelerated workloads tend to run on bare metal servers, which were typically managed separately from the rest of a company’s servers.

“With vComputeServer, IT admins can better streamline management of GPU accelerated
virtualized servers while retaining existing workflows and lowering overall operational costs,” Nvidia explains in today’s announcement. This also means that businesses will reap the cost benefits of GPU sharing and aggregation, thanks to the improved utilization this technology promises.

vComputeServer works with VMware Sphere, vCenter and vMotion, as well as VMware Cloud. Indeed, the two companies are using the same vComputeServer technology to also bring accelerated GPU services to VMware Cloud on AWS. This allows enterprises to take their containerized applications and from their own data center to the cloud as needed — and then hook into AWS’s other cloud-based technologies.

“From operational intelligence to artificial intelligence, businesses rely on GPU-accelerated computing to make fast, accurate predictions that directly impact their bottom line,” said Nvidia founder and CEO Jensen Huang . “Together with VMware, we’re designing the most advanced and highest performing GPU- accelerated hybrid cloud infrastructure to foster innovation across the enterprise.”


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Microsoft Makes It Easier to Bring DirectX 12 Games to Windows 7


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When Microsoft launched Windows 10, it made its stance on DirectX 12 clear: Windows 10 would be the only OS that supported the company’s latest API, period. For years, the company stuck to this stance. Then, earlier this year, Microsoft announced that one game — World of Warcraft — would be allowed to take advantage of the DX12 API while running Windows 7.

The reason for this allowance? Probably China. World of Warcraft has always had a huge Chinese following, and Blizzard’s decision to add DX12 support to WoW was a significant step for both the developer and the API. Now, Microsoft has announced that it’s expanding this program. In a short blog post pointing an array of API documents, Microsoft notes:

We have received warm welcome from the gaming community, and we continued to work with several game studios to further evaluate this work. To better support game developers at larger scales, we are publishing the following resources to allow game developers to run their DirectX 12 games on Windows 7.

The development guidance document for how to move DX12 to Windows 7 actually contains some useful information on how difficult it is to get games running under the older OS and what the differences are between the two. Microsoft states:

We only ported the D3D12 runtime to Windows 7. Therefore, the difference of Graphics Kernel found on Windows 7 still requires some game code changes, mainly around the presentation code path, use of monitored fences, and memory residency management (all of which will be detailed below). Early adopters reported from a few days to two weeks of work to have their D3D12 games up and running on Windows 7, though the actual engineering work required for your game may vary.

There are technical differences between DX12 on Windows 7 and DX12 on Windows 10. DirectML (Direct Machine Learning) is not supported under Windows 7, but all other features implemented in the October 2018 Windows 10 update are supported. There are differences in terms of API usage (D3D12 on Windows 7 uses different Present APIs), and some fence usage patterns are also unsupported.

There are, however, some limits to support. Only 64-bit Windows 7 with SP1 installed is supported. There’s no PIX or D3D12 debug layer on Windows 7, no shared surfaces or cross-API interop, no SLI/LDA support, no D3D12 video, and no WARP support. According to Microsoft, “HDR support is orthogonal to D3D12 and requires DXGI/Kernel/DWM functionalities on Windows 10 but not on Windows 7.” This seems to imply that HDR content can work in Windows 7, but it may be on the developer to implement it properly.

Microsoft has published additional resources on the topic, including a NuGet package and a D3D12 code sample that runs on Windows 7 and 10 with the same binary.

Why Make DX12 More Accessible?

This is honestly a little surprising to see. Windows 7 is supposed to be headed for firm retirement in a matter of months. The implication here is that Microsoft is taking this step to cater to gamers that are still using Windows 7, but the Steam Hardware Survey suggests that’s a distinct minority of gamers. Windows 10 has a 71.57 percent market share according to the SHS, while Windows 7 64-bit is pegged at 20.4 percent. What’s interesting here is that the SHS actually tilts much more towards Windows 10 than a generic OS survey.

Chinese-Desktop-Market-Share

StatCounter data puts Windows 10 at 58.63 percent of the market as of July, compared with 31.22 percent of Windows 10. This suggests that gamers tend to update their hardware more quickly than the mass market, which makes sense. But from what we’ve read, the Windows 7 gamers may be concentrated in China, where it remains the most popular OS. 49.46 percent of Chinese gamers are using Windows 7, compared with just 41.13 percent of PC gamingSEEAMAZON_ET_135 See Amazon ET commerce occurring under Windows 10. Even if we assume Chinese gamers are more likely to be using Windows 10 — and it’s not clear they are — there’s still a much larger share of users in that nation.

It’s not clear at all how Microsoft is going to deal with that problem as it relates to overall support, but it could be that this is Microsoft’s way of providing a certain degree of backward-compatibility without being willing to do anything equivalent as far as continuing to provide security features. Microsoft wants its customer base — all of it — to be Windows 10. It’s surprising to see the company extending DX12 backward, but we’d be stunned if they granted Windows 7 a stay of reprieve and kept publishing patches for it.

MS could also be hoping to encourage devs to adopt DX12 more widely. Three years after debut, neither DX12 nor Vulkan has done much to revolutionize APIs or gaming. Developers do use the APIs, but we’ve seen comparatively little use of them to pull off anything unique. The need to support older hardware and a wide range of users, plus the fact that these APIs require developers to be more familiar with the underlying hardware, seems to be a drag on their overall usage.

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Chinese Vendor Designs PCIe 4.0 GPU, Targets GTX 1080 Performance


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The high-performance GPU industry has been a two-horse race for very nearly two decades. After the collapse of 3dfx, no new company emerged to seriously challenge the ATI/Nvidia split. While Intel holds a substantial stake of the total GPU market, its integrated business has only focused on 2D, video, and basic gaming 3D. Intel’s upcoming Xe architecture, expected in 2020, will take a serious shot at breaking into the consumer space. Now, there’s a word of a potential fourth player in the field, albeit it possibly in a more specialized area.

According to THG, Jingjia Micro is a military-civilian integrated company that’s primarily focused on developing GPUsSEEAMAZON_ET_135 See Amazon ET commerce for the military market thus far. The company began by building China’s first homegrown GPU, the JM5400, built on 65nm. The success of the JM5400 allowed the company to expand and move to newer manufacturing nodes. Its next products, the JM7000 and 7200, were built on 28nm. Now, Jingjia Micro wants to expand its reach further and target the performance of the GTX 1050 and 1080 with a pair of new designs — the JM9231 and JM9271.

Jingjia-Micro-Chart

Chart by THG

A post at cnbeta has additional information. Currently, the JM7200 is said to offer performance equivalent to the GeForce GT 640, albeit in a much lower power envelope — 10W, supposedly, compared with the 50W Nvidia specced for that card. We’d like to see that claim independently verified. The OEM variant of the GT 640 was a Fermi-based part built on 40nm, but that chip had a 65W TDP. The 50W variant was a Kepler-derived part built on 28nm — the same process node Jingjia Micro uses. The JM part also supposedly has 4GB of RAM, while the GT 640 50W version had just 1GB of GDDR5.

The JM9231 and JM9271 are supposedly the first fully programmable GPUs that Jingjia Micro has developed; there are references to the previous JM5400 and JM7200 families being based on fixed-function rendering pipelines. These limitations wouldn’t fly under modern APIs for Windows, but the company started life as a military GPU vendor, and such applications obviously have very different requirements for APIs and product certification.

The new JM parts obviously aren’t going to gun for the highest-end cards from Nvidia or AMD, but even approaching high-end performance from 2016 – 2017 would allow them to contend for the midrange and budget markets. Bringing up the software stack and winning developer support would obviously be critical to any market play, and there doesn’t seem to be any information about whether the JM9231 or JM9271 include any performance improvements or ideas that we haven’t seen before from the major vendors. Such events are rare, but not unheard of. PowerVR once attempted to establish itself as a third player in PC graphics with the Kyro and Kyro II, which won some market share for itself as a unique solution with higher memory bandwidth efficiency than either ATI or Nvidia.

The use of HBM memory in a product of this sort is rather interesting, as is the comparatively low memory bandwidth (by HBM standards). Given that both products lack modern API support, it’s possible they’re intended strictly for military use — though in that case, referencing the GTX 1080 would be a bit odd. Either way, China clearly has its eye on competing more aggressively in terms of overall silicon performance. A few more years, and we might see new products from vendors we haven’t seen before challenging ‘homegrown’ alternatives like AMD, Nvidia, and (if its Xe launch goes well), Intel.

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Minecraft Will Receive Nvidia-Exclusive RTX Update for Raytracing Support


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Last week, Mojang announced that the Minecraft Super Duper Graphics Pack (SDGP) that Microsoft had promised back in 2017 was canceled, permanently. No additional information was offered on why the update was canceled, or what the team had been working on for the past two years before canceling it. As of today, Nvidia has announced that it will be developing an exclusive ray tracing pack for Minecraft for Nvidia GPU owners.

Mojang’s announcement states: “It’ll be playable on Windows 10 with devices that are capable of DirectX R, such as with an NVIDIA GeForce RTX GPU (and we plan to expand it to future platforms that support DirectX R raytracing).”

Presumably, DirectX R is a reference to DXR, which is the official name for DirectX ray tracing. RTX is Nvidia’s brand-name for its implementation of the Microsoft DXR standard. We imagine AMD will introduce its own branding or simply use the DXR moniker when it launches support. Speaking of AMD, we don’t know if the “future platforms” is a reference to systems like the PS5 and Xbox Next or whether it’s a reference to Big Navi, which is expected to debut next year.

“Ray tracing sits at the center of what we think is next for Minecraft,” said Saxs Persson, head creative director for Minecraft at Microsoft. “GeForce RTX gives the Minecraft world a brand-new feel to it. In normal Minecraft, a block of gold just appears yellow, but with ray tracing turned on, you really get to see the specular highlight, you get to see the reflection, you can even see a mob reflected in it.”

Minecraft-RTX-versus-Off

The screenshot above shows the impact of having RTX enabled versus disabled, with the divider halfway down the screen. RTX-enabled is on the right.

There’s likely a relationship between the cancellation of the SDGP and Nvidia’s announcement that it will provide a path tracing solution for Minecraft, but there’s no indication of what the exact relationship was. The SDGP was in development for a long time and Mojang was always very tight-lipped about its progress. Developer communication on this point, when it occurred, amounted to “nothing to report, full engine updates are very complicated and you’ll see it when it’s closer to done than it is now.”

It’s possible that Nvidia saw the work being done by third-party modders to integrate ray tracing into Minecraft, was inspired by it, and reached out to Mojang about the possibility directly. But even if Mojang liked the idea, it’s highly unlikely that the company would cancel two years worth of work on its own overhaul. But all of this is rather confusing because Mojang announced the launch of its own new rendering pipeline as part of its overall RTX graphics cardSEEAMAZON_ET_135 See Amazon ET commerce support. The Bedrock version of Minecraft will be updated with a new graphics rendering pipeline, codenamed Render Dragon.

In other words: When Microsoft announced the Super Duper Graphics Pack, it announced that the SDGP would be introduced alongside a new graphics engine implemented in the Bedrock version of the game. Earlier this year, we covered an extensive path tracing Minecraft mod that turned the game into a vision of beauty. Now, Nvidia has cut a deal with Mojang to build an RTX-powered version of the game and Mojang has killed the SDGP, but will still introduce the updated rendering engine the SDGP was expected to use.

If we had to guess, we’d guess that Mojang’s comments about DXR compatibility refer to other consoles, or at least the Xbox Next. Nvidia isn’t going to do any work to provide ray tracing support on a non-Nvidia GPU, but Microsoft is unlikely to invest heavily in a ray-tracing update for Minecraft that can’t run on its own Xbox Next. Whatever gets hammered out between them, it wouldn’t surprise us if AMD GPU customers who don’t game on consoles are out of luck with this one. Modders have already created a path-traced version of Minecraft that looks extraordinary, so at least third-party options will continue to be available.

The RTX-powered version of Minecraft should arrive sometime in 2020. Hopefully, Nvidia will pay better attention to performance optimization than it did in Quake II — that particular game runs quite slowly, despite the age of the underlying engine. Minecraft’s greatest strength is its ability to play on anything, and as great as it is to see features like path tracing, we hope Nvidia can offer them without destroying performance. Features like 4K support, which was a major capability of the SDGP, have not been mentioned.

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Minecraft to get big lighting, shadow and color upgrades through Nvidia ray tracing – gpgmail


Minecraft is getting a free update that brings much-improved lighting and color to the game’s blocky graphics using real-time ray tracing running on Nvidia GeForce RTX graphics hardware. The new look is a dramatic change in the atmospherics of the game, and manages to be eerily realistic while retaining Minecraft’s pixelated charm.

The ray tracing tech will be available via a free update to the game on Windows 10 PCs, but it’ll only be accessible to players using an Nvidia GeForce RTX GPU, since that’s the only graphics hardware on the market that currently supports playing games with real-time ray tracing active.

It sounds like it’ll be an excellent addition to the experience for players who are equipped with the right hardware, however – including lighting effects not only from the sun, but also from in-game materials like glowstone and lava; both hard and soft shadows depending on transparency of material and angle of light refraction; and accurate reflections in surfaces that are supposed to be reflective (ie. gold blocks, for instance).

This is welcome news after Minecraft developer Mojang announced last week that it cancelled plans to release its Super Duper Graphics Pack, which was going to add a bunch of improved visuals to the game, because it wouldn’t work well across platforms. At the time, Mojang said it would be sharing news about graphics optimization for some platforms “very soon,” and it looks like this is what they had in mind.

Nvidia meanwhile is showing off a range of 2019 games with real-time ray tracing enabled at Gamescom 2019 in Cologne, Germany, including Dying Light 2, Cyperpunk 2077, Call of Duty: Modern Warfare and Watch Dogs: Legion.


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Chinese Foundry SMIC Begins 14nm Production


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One of the longstanding trends in semiconductor manufacturing has been a steady decrease in major foundry players. Twenty years ago, when 180nm manufacturing was cutting-edge technology, there were no fewer than 28 firms deploying the node. Today, there are three companies building 7nm technology — Samsung, TSMC, and Intel. A fourth, GlobalFoundries, has since quit the cutting-edge business to focus on specialty foundry technologies like its 22nm and 12nm FDX technology.

What sometimes gets lost in this discussion, however, is the existence of a secondary group of foundry companies that do deploy new nodes — just not at the cutting-edge of technological research. China’s Semiconductor Manufacturing International Corporation (SMIC) has announced that it will begin recognizing 14nm revenue from volume production by the end of 2019, a little more than five years after Intel began shipping on this node. TSMC, Samsung, and GlobalFoundries all have extensive 14nm capability in production, as does UMC, which introduced the node in 2017.

Secondary sources for a node, like UMC and SMIC, often aren’t captured in comparative manufacturing charts like the one below because the companies in question offer these nodes after they’ve been deployed as cutting-edge products by major foundries. In many cases, they’re tapped by smaller customers with products that don’t make news headlines.

FoundryManufacturing

SMIC, however, is something of a special case. SMIC is mainland China’s largest semiconductor manufacturer and builds chips ranging from 350nm to 14nm. The company has two factories with the ability to process 300mm wafers, but while moving to 14nm is a major part of China’s long-term semiconductor initiative, SMIC isn’t expected to have much 14nm capacity any time soon. The company’s high utilization rate (~94 percent) precludes it having much additional capacity to dedicate to 14nm production. SMIC is vital to China’s long-term manufacturing goals; the country’s “Made in China 2025” plan calls for 70 percent of its domestic semiconductor demand to come from local companies by 2025. Boosting production at SMIC and bringing new product lines online is vital to that goal. That distinguishes the company from a foundry like UMC, which has generally chosen not to compete with TSMC for leading-edge process nodes. SMIC wants that business — it just can’t compete for it yet.

Dr. Zhao Haijun and Dr. Liang Mong Song, SMIC’s Co-Chief Executive Officers released a statement on the company’s 14nm ramp, saying:

FinFET research and development continues to accelerate. Our 14nm is in risk production and is expected to contribute meaningful revenue by year-end. In addition, our second-generation FinFET N+1 has already begun customer engagement. We maintain long-term and steady cooperation with customers and clutch onto the opportunities emerging from 5G, IoT, automotive and other industry trends.

Currently, only 16 percent of the semiconductors used in China are built there, but the country is adding semiconductor production capacity faster than anywhere else on Earth. The company is investing in a $10B fab that will be used for dedicated 14nm production. SMIC is already installing equipment in the completed building, so production should ramp up in that facility in 2020. Once online, the company will have significantly more 14nm capacity at its disposal (major known customers of SMIC include HiSilicon and Qualcomm). Texas Instruments has built with the company in the past (it isn’t clear if it still does), as has Broadcom. TSMC and SMIC have gone through several rounds of litigation over IP misappropriation; both cases were settled out of court with substantial payments to TSMC.

Despite this spending, analysts do not expect SMIC to immediately catch up with major foundry players from other countries; analysts told CNBC it would take a decade for the firm to close the gap with other major players. Exact dimensions on SMIC’s 14nm node are unknown. Foundry nodes are defined by the individual company not by any overarching standard organization or in reference to any specific metric. Those looking for additional information on that topic will find it here.

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The renaissance of silicon will create industry giants – gpgmail


Every time we binge on Netflix or install a new internet-connected doorbell to our home, we’re adding to a tidal wave of data. In just 10 years, bandwidth consumption has increased 100 fold, and it will only grow as we layer on the demands of artificial intelligence, virtual reality, robotics and self-driving cars. According to Intel, a single robo car will generate 4 terabytes of data in 90 minutes of driving. That’s more than 3 billion times the amount of data people use chatting, watching videos and engaging in other internet pastimes over a similar period.

Tech companies have responded by building massive data centers full of servers. But growth in data consumption is outpacing even the most ambitious infrastructure build outs. The bottom line: We’re not going to meet the increasing demand for data processing by relying on the same technology that got us here.

The key to data processing is, of course, semiconductors, the transistor-filled chips that power today’s computing industry. For the last several decades, engineers have been able to squeeze more and more transistors onto smaller and smaller silicon wafers — an Intel chip today now squeezes more than 1 billion transistors on a millimeter-sized piece of silicon.

This trend is commonly known as Moore’s Law, for the Intel co-founder Gordon Moore and his famous 1965 observation that the number of transistors on a chip doubles every year (later revised to every two years), thereby doubling the speed and capability of computers.

This exponential growth of power on ever-smaller chips has reliably driven our technology for the past 50 years or so. But Moore’s Law is coming to an end, due to an even more immutable law: material physics. It simply isn’t possible to squeeze more transistors onto the tiny silicon wafers that make up today’s processors.

Compounding matters, the general-purpose chip architecture in wide use today, known as x86, which has brought us to this point, isn’t optimized for computing applications that are now becoming popular.

That means we need a new computing architecture. Or, more likely, multiple new computer architectures. In fact, I predict that over the next few years we will see a flowering of new silicon architectures and designs that are built and optimized for specialized functions, including data intensity, the performance needs of artificial intelligence and machine learning and the low-power needs of so-called edge computing devices.

The new architects

We’re already seeing the roots of these newly specialized architectures on several fronts. These include Graphic Processing Units from Nvidia, Field Programmable Gate Arrays from Xilinx and Altera (acquired by Intel), smart network interface cards from Mellanox (acquired by Nvidia) and a new category of programmable processor called a Data Processing Unit (DPU) from Fungible, a startup Mayfield invested in.  DPUs are purpose-built to run all data-intensive workloads (networking, security, storage) and Fungible combines it with a full-stack platform for cloud data centers that works alongside the old workhorse CPU.

These and other purpose-designed silicon will become the engines for one or more workload-specific applications — everything from security to smart doorbells to driverless cars to data centers. And there will be new players in the market to drive these innovations and adoptions. In fact, over the next five years, I believe we’ll see entirely new semiconductor leaders emerge as these services grow and their performance becomes more critical.

Let’s start with the computing powerhouses of our increasingly connected age: data centers.

More and more, storage and computing are being done at the edge; that means, closer to where our devices need them. These include things like the facial recognition software in our doorbells or in-cloud gaming that’s rendered on our VR goggles. Edge computing allows these and other processes to happen within 10 milliseconds or less, which makes them more work for end users.

I commend the entrepreneurs who are putting the silicon back into Silicon Valley.

With the current arithmetic computations of x86 CPU architecture, deploying data services at scale, or at larger volumes, can be a challenge. Driverless cars need massive, data-center-level agility and speed. You don’t want a car buffering when a pedestrian is in the crosswalk. As our workload infrastructure — and the needs of things like driverless cars — becomes ever more data-centric (storing, retrieving and moving large data sets across machines), it requires a new kind of microprocessor.

Another area that requires new processing architectures is artificial intelligence, both in training AI and running inference (the process AI uses to infer things about data, like a smart doorbell recognizing the difference between an in-law and an intruder). Graphic Processing Units (GPUs), which were originally developed to handle gaming, have proven faster and more efficient at AI training and inference than traditional CPUs.

But in order to process AI workloads (both training and inference), for image classification, object detection, facial recognition and driverless cars, we will need specialized AI processors. The math needed to run these algorithms requires vector processing and floating-point computations at dramatically higher performance than general purpose CPUs provide.

Several startups are working on AI-specific chips, including SambaNova, Graphcore and Habana Labs. These companies have built new AI-specific chips for machine intelligence. They lower the cost of accelerating AI applications and dramatically increase performance. Conveniently, they also provide a software platform for use with their hardware. Of course, the big AI players like Google (with its custom Tensor Processing Unit chips) and Amazon (which has created an AI chip for its Echo smart speaker) are also creating their own architectures.

Finally, we have our proliferation of connected gadgets, also known as the Internet of Things (IoT). Many of our personal and home tools (such as thermostats, smoke detectors, toothbrushes and toasters) operate on ultra-low power.

The ARM processor, which is a family of CPUs, will be tasked for these roles. That’s because gadgets do not require computing complexity or a lot of power. The ARM architecture is perfectly designed for them. It’s made to handle smaller number of computing instructions, can operate at higher speeds (churning through many millions of instructions per second) and do it at a fraction of the power required for performing complex instructions. I even predict that ARM-based server microprocessors will finally become a reality in cloud data centers.

So with all the new work being done in silicon, we seem to be finally getting back to our original roots. I commend the entrepreneurs who are putting the silicon back into Silicon Valley. And I predict they will create new semiconductor giants.


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