compute module 4

17 Articles

Adventures In Overclocking: Which Raspberry Pi 4 Flavor Is Fastest?

November 11, 2020 by 56 Comments

There are three different versions of the Raspberry Pi 4 out on the market right now: the “normal” Pi 4 Model B, the Compute Module 4, and the just-released Raspberry Pi 400 computer-in-a-keyboard. They’re all riffing on the same tune, but there are enough differences among them that you might be richer for the choice.

The Pi 4B is easiest to integrate into projects, the CM4 is easiest to break out all the system’s features if you’re designing your own PCB, and the Pi 400 is seemingly aimed at the consumer market, but it has a dark secret: it’s an overclocking monster capable of running full-out at 2.15 GHz indefinitely in its stock configuration.

In retrospect, there were hints dropped everywhere. The system-on-a-chip that runs the show on the Model B is a Broadcom 2711ZPKFSB06B0T, while the SOC on the CM4 and Pi 400 is a 2711ZPKFSB06C0T. If you squint just right, you can make out the revision change from “B” to “C”. And in the CM4 datasheet, there’s a throwaway sentence about it running more efficiently than the Model B. And when I looked inside the Pi 400, there was this giant aluminum heat spreader attached to the SOC, presumably to keep it from overheating within the tight keyboard case. But there was one more clue: the Pi 400 comes clocked by default at 1.8 GHz, instead of 1.5 GHz for the other two, which are sold without a heat-sink.

Can the CM4 keep up with the Pi 400 with a little added aluminum? Will the newer siblings leave the Pi 4 Model B in the dust? Time to play a little overclocking!

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Trying (And Failing) To Use GPUs With The Compute Module 4

November 10, 2020 by 15 Comments

The Raspberry Pi platform grows more capable and powerful with each iteration. With that said, they’re still not the go-to for high powered computing, and their external interfaces are limited for reasons of cost and scope. Despite this, people like [Jeff Geerling] strive to push the platform to its limits on a regular basis. Unfortunately, [Jeff’s] recent experiments with GPUs hit a hard stop that he’s as yet unable to overcome.

With the release of the new Compute Module 4, the Raspberry Pi ecosystem now has a device that has a PCI-Express 2.0 1x interface as stock. This lead to many questioning whether or not GPUs could be used with the hardware. [Jeff] was determined to find out, buying a pair of older ATI and NVIDIA GPUs to play with.

Immediate results were underwhelming, with no output whatsoever after plugging the modules in. Of course, [Jeff] didn’t expect things to be plug and play, so dug into the kernel messages to find out where the problems lay. The first problem was the Pi’s limited Base Address Space; GPUs need a significant chunk of memory allocated in the BAR to work. With the CM4’s BAR expanded from 64MB to 1GB, the cards appeared to be properly recognised and ARM drivers were able to be installed.

Alas, the story ends for now without success. Both NVIDIA and ATI drivers failed to properly initialise the cards. The latter driver throws an error due to the Raspberry Pi failing to account for the I/O BAR space, a legacy x86 feature, however others suggest the problem may lay elsewhere. While [Jeff] may not have pulled off the feat yet, he got close, and we suspect with a little more work the community will find a solution. Given ARM drivers exist for these GPUs, we’re sure it’s just a matter of time.

For more of a breakdown on the Compute Module 4, check out our comprehensive article. Video after the break.

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Getting Over 4Gbps Out Of A Compute Module 4

November 9, 2020 by 30 Comments

For the average home gamer, good old fashioned Ethernet at 100 Mbit/s is only just starting to become a bottleneck as things like 4K video streaming begin to demand more bandwidth. As always, though, there are those who wish to push the limits of what is possible. [Jeff Geerling] is one such operator, who set out to maximise the network throughput on the Raspberry Pi Compute Module 4. 

The build began by taking advantage of the PCI-Express 2.0 single lane interface on the new Raspberry Pi Compute Module. Hooked up to an Intel four-port Gigabit Ethernet card, and in combination with the onboard Gigabit-E port, [Jeff] was able to get 3.0 Gbit/s out of the setup without too much fuss. However, he wanted more, and set about finding where he was being held back. It turned out that ksoftirqd, a daemon that handles network packets, can only run on one core on the Raspberry Pi 4, and it was getting maxed out at this data rate. Overclocking the CPU helped, getting the max rate up to 3.4 Gbit/s.

Further analysis showed that the onboard interface was only contributing 200 Mbit/s, with the Intel card maxing out at 3.2 Gbit/s. In the case of the latter, this was due to the limits of the PCI-E interface. In the case of the former, however, [Jeff] knew that more was available. The trick turned out to be recompiling the Linux kernel to allow the internal interface to be able to set to use a higher Maximum Transmission Unit. This allows each network transmission to carry more data without extra CPU load. With the internal interface and the external card all set to an MTU of 9000, the Pi was able to spit out a scorching 4.15 Gbit/second. Details of the hack are available on Github for the curious.

It’s a hack that doesn’t offer a lot to the average user, though [Jeff] states he has some interesting applications in mind. He’s also contemplating what can be achieved with a 10 Gbit card, which we can’t wait to see. If you want to learn more about the Compute Module’s features, including a couple of tips for laying out yor own board, check out our review. Video after the break.

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