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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AMD, Nvidia High-End GPUs Are Much Better Deals Now Than 6 Months Ago


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Back in February, I wrote a story about how AMD and Nvidia had collectively launched the least-appealing high-end GPU refresh cycle in the history of the gaming industry. After the launch of AMD’s Navi 5700 and 5700 XT, and Nvidia’s rejoinder with the RTX 2060 Super and 2070 Super, it makes sense to revisit that conclusion. How much have things improved, just over half a year later?

They’ve actually improved a lot if you’re buying at the upper end of the market. Before we examine the specifics of the changes, let me clarify some terms. Historically, GPU price brackets look something like this:

Budget: $150 or less.
Midrange: $150 – $300
High-End: $300 – $500
Ultra-High: $500+.

When Nvidia introduced the RTX family, it significantly raised prices. Instead of the GTX 1070 around $370 and the GTX 1080 at $500 – $550, the RTX 2070 was a $500 GPU, the RTX 2080 cost $700, and the 2080 Ti effectively ran $1,100 – $1,200 ($1,000 technically, but nobody ever had them for this, as far as I can tell).

There are two basic ways for a publication like ours to handle this: Hold its own price banding, and fit the new cards into it, or modify our price bands and raise them to accommodate the manufacturer. If you take the latter approach, AMD’s Navi GPUs are now “midrange” cards, despite carrying price tags of $350 and $400. This is also how you wind up with articles referring to the iPhone XR as “entry-level,” or “budget” at $750 as if Apple hadn’t just killed the only pseudo-budget device it offered, the $350 iPhone SE.

Adjusting price bands to reflect what companies are selling isn’t wrong, so long as it tracks with what customers are buying. Nvidia’s upcoming Q2 numbers should provide more confirmation here, but available data suggested Turing sales badly lagged Pascal at launch and may not have recovered since. If Nvidia truly thought it had established ray tracing as a feature gamers were willing to pay for, it wouldn’t have cut pricing on its RTX 2060, 2070, and 2080 GPUs at all.

So far as ExtremeTech is concerned, at least for now, the Navi 5700 and 5700 XT are high-end cards, as are the RTX 2060, 2060 Super, 2070, and 2070 Super. The RTX 2080, 2080 Super, and 2080 Ti belong to their own, separate category of ultra-high-end devices.

Evaluating the Improvement

We recently measured long-term performance evolution in a variety of GPUs, but we can use that data set for a different purpose. Keep in mind that in the graph series below, the GeForce RTX 2080 (non-Super) offers roughly identical performance to the RTX 2070 SuperSEEAMAZON_ET_135 See Amazon ET commerce (the 2070S is typically within 95 to 105 percent the performance of the RTX 2080).

Comparing RTX 2070S/2080 against GTX 1080, we see minimum frame rates are 1.18x higher at 1080p, 1.28x higher at 1440p, and 1.4x higher at 4K. Average frame rates across our entire suite of games are 1.3x higher at 1080p, 1.4x higher at 1440p, and 1.44x higher at 4K.

I don’t have the same level of data on the GTX 1070 to compare with the RTX 2060 Super, but we know that the 2060S improves performance by about 1.15x as well, that it performs nearly identically to the original RTX 2070, and that the GPU’s new $400 price point puts it closer to the original GTX 1070 than the OG 1080 price.

As for AMD, the 5700 and 5700 XTSEEAMAZON_ET_135 See Amazon ET commerce are effectively a replacement for the Vega 56 and Vega 64. The slideshow below contains the results from our RX 5700 and 5700 XT review. The Radeon RX 5700 matches Vega 64 in virtually every test, but costs $350 as opposed to $500. It draws 74 percent as much power while outperforming the RTX 2060.

As upgrades for existing Vega 56 and Vega 64 owners, the best case is going to be between Vega 56 and RX 5700 XT. In this case, I’m estimating the gains of doing so, but I’m fairly sure they aren’t as large as the improvements between Pascal and Turing at Turing’s adjusted prices. Vega 56 was typically 1.08x – 1.12x slower than Vega 64, but the 5700 XT’s lead over Vega 64 varies significantly depending on the game. In a few cases, the two GPUs are tied.

AMD gamers with older cards or Nvidia gamers looking to switch sides are the more likely customers for RX 5700 and RX 5700 XT, and the performance these cards offer makes them a potentially attractive upgrade in these markets.

A Significant Improvement

AMD’s new launches have restored a better, more consumer-friendly balance to the upper end of the GPU market. The ultra-high-end market remains less friendly. The RTX 2080 Super offers the smallest performance improvement of all the “Super” cards and does not do a very good job of justifying its $200 price premium over the RTX 2070 Super. Both the Radeon VII and RTX 2080 Super are only justifiable if you’re actually gaming in 4K, and honestly, they aren’t all that compelling even in that situation.

AMD has said nothing about its plans for the midrange market yet, but the company must be working on cards to refresh this space as well. Hopefully, it won’t be long before we have far more power-efficient, higher-performing chips ready to take the place of the RX 570, 580, and 590.

As for whether Navi or Turing is a better upgrade path, that’s going to depend a bit on what you want: A bit more speed (relative to the competition), or features like ray tracing? Some users may not feel that even these gains are sufficient, which I understand. But we can at least say that there are gains in performance/dollar relative to the previous generation. Six months ago, it wasn’t possible to make that claim.

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