malig72能比较(来自未来的科技)

vcbgfh8RQW 2024-04-14

一、armmalig72mp3是什么处理器

armmalig72mp3是GPU处理器。GPU即图形处理器，又称显示核心、视觉处理器、显示芯片，是一种专门在个人电脑、工作站、游戏机和一些移动设备如平板电脑、智能手机等上图像运算工作的微处理器。我们通常就叫它显卡，GPU是显示卡的大脑，它决定了该显卡的档次和大部分性能，对于传统PC上来说，GPU同时也是2D显示卡和3D显示卡的区别依据。

armmalig72mp3处理器的作用

armmalig72mp3处理器的作用是将计算机系统所需要的显示信息进行转换驱动，并向显示器提供行扫描信号，控制显示器的正确显示，是连接显示器和个人电脑主板的重要元件，也是人机对话的重要设备之一。显卡作为电脑主机里的一个重要组成部分，承担输出显示图形的任务，对于从事专业图形设计的人来说显卡非常重要。

二、MaliG72来自未来的科技

本文翻译自： Mali-G72– Enabling tomorrow’s technology today

You might have noticed that around this time of year we start to talk about our latest high end GPU. Well, 2017 is no different and we in the Arm Mali team are delighted to welcome Mali-G72 to the High Performance GPU roadmap.

你们也许注意到了我们今年开始谈论高端GPU，我们很高兴在这个特殊的2017年推出我们的高端GPU：Mali-G72

Following on from Mali-G71 released last year, Mali-G72 was launched at Computex 2017 and builds on the introduction of the awesome Bifrost architecture to provide even greater performance within an ever smaller area and power budget. Designed for High Fidelity Mobile Gaming and the emerging field of Machine Learning(ML) on device, Mali-G72 also takes the VR capability of Mali-G71 to a whole new level. Mali-G72 based devices will have 1.4x the overall graphics performance compared to devices based on its predecessor, guaranteeing it’s ready to meet the needs of whatever fantastic new tech hits the industry next.

继去年推出Mali-G71，我们在Computex 2017上推出了Mali-G72。Mali-G72采用的仍然是Bifrost架构，在提升了性能的同时还减少了芯片面积和功耗。Mali-G72支持移动设备上高保真的游戏、机器学习、虚拟现实。装载Mali-G72的设备与之前的相比，图形性能提升了1.4倍。

Mali-G72 highlights:

Mali-G72的亮点：

As I mentioned before, one of the major driving forces behind Mali-G72 is the rise of High Fidelity Gaming on mobile. Whilst there is still a huge market for casual games like Candy Crush, we’re seeing more and more growth in the revenue generated by complex games, with 43% of the Chinese mobile gaming industry now made up of these titles. Photorealistic visuals, like the ones in Digital Legends’ First person shooter, Afterpulse, used to be impossible on mobile. The power consumption of high vertex counts, numerous draw calls and more complex vertex and fragment shaders, as well as advanced graphics effects like dynamic shadows, was simply too high for the mobile form factor and reduced both quality and playtime. We consult and collaborate with our incredible ecosystem of partners and developers to ensure our newest products meet the needs of the market no matter their individual priorities. We worked closely with Digital Legends to ensure that the latest advanced rendering techniques could be supported alongside our fantastic optimization tools to maximise both performance and efficiency and were able to attain a 42% write bandwidth saving over Mali-G71. Add in the use of Pixel Local Storage(PLS) and you can save an additional 45%, making a total read bandwidth saving of 68%. It’s this collaboration which breeds innovations like those in the Mali-G72 and makes feature-rich games like Afterpulse a reality for mobile gamers.

就像我之前提到过的，驱动Mali-G72推出的背后动力是移动设备上高端游戏的兴起。在普通游戏（比如Digital Legends）仍旧占据巨大市场份额的时候，可以看到复杂游戏的份额正在逐渐增加，像吃鸡这样的第一人称设计游戏，在以前来看是不可能出现在移动设备上的，因为巨量的顶点和绘制调用、复杂的着色程序、高级的显示效果（如动态变化的影子等）需要产生大量的功耗，继而会影响游戏体验和手机续航。我们和Digital Legends紧密合作，不仅支持了最先进的渲染技术，而且使用了强大的优化工具，使得性能和效率最大化，写带宽降低了42%（与Mali-G71相比），如果启用Pixel Local Stroage，可以进一步降低45%的写带宽、68%的读带宽。正式这次合作促使了Mali-G72中的创新，并且这些创新使得复杂游戏在移动设备中可以流畅运行。

VR is evolving too, and we knew we needed to up our game even further to continue to lead this exciting market. More than 50% of existing mobile VR devices are powered by Mali, and the Mali-powered Mate 9 is one of the first Daydream certified VR devices available, so continuous innovation is a top priority. As you might have seen in our recent Circuit VR demo, released at GDC 2017, we’ve been working on techniques such as mobile Multiview to reduce the overhead of drawing things multiple times as you typically need to in VR(where you effectively need one complete render per eye). Add in foveated rendering, where you only see the section of the image directly in line with your fovea in high resolution, and you suddenly have four or more views to render and Multiview really comes to its own. Other techniques like Multi Sample Anti-Aliasing(MSAA) add blended pixels to either side of a line which should appear smooth in order to reduce the jagged effect which can sometimes be seen in the close quarters of the VR headset. Mali-G72 enables 8 or 16 x MSAA at minimal system cost. All of this of course comes on top of already existing, clever innovations such as Adaptive Scalable Texture Compression(ASTC), allowing us to incorporate higher quality textures without compromising on the amount of bandwidth used.

I also mentioned earlier that Machine Learning is another key use case on mobile, but let me clarify what I mean by this. Today, ML is often performed in the cloud, with large data sets used to train neural networks to begin to make intelligent connections, but more and more needs to happen on device. Not only is it costly to keep transferring large amounts of data to the cloud for simple applications like translation, but it’s also slow. I don’t know about you, but I don’t have much time for latency. I expect my smartphone to do what I want, when I need it, and waiting for a connection or data transfer can put me off using even the best applications. This is why the focus is very much on directing ML inference to the device itself. Huawei have already seen the need for this in their latest premium device, the Mate 9, powered by the Mali-G71 and released a record breaking 8 months after they first received the product. In the Mate 9, the ML algorithm establishes which applications you use the most and intelligently prioritizes the power and performance to make sure they perform at their best. Mali-G71 with its innovative Bifrost architecture is already pretty good at ML inference, as you can see in the chart below– the Mali-G71 MP8 in the Huawei Mate 9 handles AlexNet 87% quicker than a low-end discrete graphics card, which has comparable graphics performance.

Well, Mali-G72 is even better. The arithmetic optimizations and increased caches we look at later really come into their own here, reducing bandwidth to such an extent that Mali-G72 can provide the most efficient and performant ML possible. So how do we support these use cases?

Retaining Bifrost’s key high performance features such as full system coherency between the CPU and GPU, as well as index-driven position shading, clause-based execution and quads; Mali-G72 packs a few new punches too. Optimizations in the arithmetic efficiency as well as enhanced capabilities for both complex graphics performance and scalability, make Mali-G72 the obvious choice for next year’s premium mobile products across smartphone, VR, ML and many other opportunities. But what exactly have we done?

在保留Bifrost架构之前的关键特性的情况下，Mali-G72推出了几个新的优化：提升了算术运算的效率、复杂图形渲染下的效率和可扩展性，接下来将详细介绍，在这之前，回顾一下上面提到的关键特性：

We’ve increased tile buffer memory in order to allow the GPU to support more storage per active tile. This increases throughput in light loading situations as well as allowing greater utilization of Multi Sample Anti-Aliasing(MSAA) and Pixel Local Storage(PLS) and providing significant improvements to performance and visual quality. We’ve also rebalanced the execution engine data path to remove some rarely-used instructions and replace them with sequences of simpler instructions to reduce both area and power, lowering cost of implementation for our partners and increasing efficiency throughout the system. To support higher graphics complexity we’ve optimized the more complex operations, such as reciprocal square root, that are used most frequently, and increased caches in the tiler for better throughput. These changes improve performance scaling in high performing systems and provide a better graphics experience to the end user. In order to further reduce bandwidth we’ve increased the size of both the Level 1 cache and the writeback cache, as well as changing the instruction cache logic to allow better utilization and reduce cache misses in complex content without increasing the overall area or power. This careful balance of performance and efficiency is vital to those partners targeting a range of devices.

我们增大了Tile缓存，以让每个Tile可以有更多的存储空间。这样可以增加加载光源数据时的数据吞吐量、可以更充分的利用MSAA和PLS，从而显著提升性能以及渲染后的图像质量。我们还调整了Execution Engine中的data path，主要改动有：移除了指令集中很少使用到的复杂指令，取而代之的是几条简单指令的组合，这样之前处理复杂指令的硬件也可以移除，从而减小芯片面积和功耗、提升系统的数据吞吐量。为了支持高复杂度的渲染，我们还优化了使用频率最高的反平方根、增加了Tiler中Cache的大小以增大数据吞吐能力。

这些改变提升了性能并给终端用户以更好的体验。

为了更进一步的降低数据带宽，我们增加了L1 Cache和Writeback Cache的大小，修改了指令Cache的逻辑以更好的利用它，并减小Cache丢失率，而这个逻辑改变并没有增加面积和功耗。

Mali-G72, with its many innovations on the Bifrost architecture, has achieved some serious gains over the previous generation product, including 25% higher energy efficiency, 20% more performance per mm2 of silicon and 17% more efficiency for Machine Learning. With all this AND 40% more in-device performance overall, it’s only a matter of time until we see the Mali-G72 exceeding our expectations in next year’s premium mobile devices.

Mali-G72作为Bifrost架构的第二代GPU，与之前的Mali-G71相比，能耗效率提升了25%、每平米毫米的硅片面积的性能提升了20%以及机器学习性能提升了17%。而且在GPU集成之后，总体性能提升了40%。