It’s easy to talk about a lot of architectural benefits and features, but ultimately the proof is in actual measurable recognisable efficiency benefits. It’s for this reason that our design teams make use of hundreds of focus tests to ensure that the hardware is able to sustain peak throughput performance in a wide range of usage scenarios.
Equally similar tests are also used to ensure optimal power efficiency. From an architectural point of view, our aim is to always improve: execute the same workload (e.g. render a certain frame) in the same (worst case) or better in a lower power budget (design target) when compared to our previous GPU generation.
To illustrate this, the graph below shows fillrate efficiency calculated based on independent measured fillrate data from Kishonti’s GFXBench suite.
This fillrate is then compared with the fillrate throughput claimed by marketing. Basically if a certain product is measured at 500MPixels/second in the benchmark yet the marketing material indicates a 250MHz core with 4 pixels per clock, then the design is 50% efficient – from 500MPixels (measured) versus 1000MPixels (expected).
In this graph, the purple bars represent PowerVR products, and the other colours represent a range of competitive products. As can be seen, the majority of PowerVR GPUs (a range from SGX Series5 and Series5XT to Series6 products) deliver real world sustained rates above 80%, only one product with a very high clock frequency sits at just below 70% efficiency, whereas the bulk of competitor products sit at efficiency rates below 60% and typically even below 50%.
Now what this means is that PowerVR products put down logic to deliver a certain fillrate and typically 80% of that fillrate can be sustained and delivered – basically a good return on silicon area and power investment. For competitive products, silicon area and power is being invested but the return is only 50%. It’s a bit like paying for 200g of chocolate but only getting 100g in your package… not something you’d be very happy about.
The above graph nicely sums up our architectural focus on efficiency, and how this results in high performance and high power efficiency, and thus the best overall results in practical real world applications.
In my next blog post, I will discuss how we write and optimize software for PowerVR GPUs, including drivers and software stacks .
If you have any questions or feedback about Imagination’s graphics IP, please use the comments box below. To keep up to date with the latest developments on PowerVR, follow us on Twitter (@GPUCompute, @PowerVRInsider and @ImaginationTech) and subscribe to our blog feed.
‘Understanding PowerVR’ is an on-going, multi-part series of blog posts from Kristof Beets, Imagination’s Senior Business Development Manager for PowerVR. These articles not only focus on the features that make PowerVR GPUs great, but also provide a detailed look at graphics hardware architectures and software ecosystems in mobile markets.
If you’ve missed any of the posts, here are some backlinks:
- PowerVR GPUs and graphics API standards (part 1)
- PowerVR GPUs and graphics API standards (part 2)
- PowerVR and GPU compute (part 3)
- PowerVR, TBDR and architecture efficiency (part 4)
- Multithreading, multitasking ALUs, the MicroKernel and core scalability (part 5)
- PVRTC, PVRTC2 and texture compression (part 6)
- YUV colour space conversions and the 2D core (part 7)
- PowerVR’s market leading fillrate efficiency (part 8)
- PowerVR’s hardware is nothing without software optimization (part 9)
- The PowerVR Insider ecosystem and final thoughts (part 10)