Posts by Dirk Broer

1) Questions and Answers : Unix/Linux : /lib64/libc.so.6: version `GLIBC_2.18' not found (Message 1454)
Posted 19 Dec 2021 by Dirk Broer
Post:
Try running CentOS 8.5.2111
2) Questions and Answers : Issue Discussion : Restriction based on GLIBC version? (Message 1453)
Posted 19 Dec 2021 by Dirk Broer
Post:
Any progress on this issue or did you give up on CentOS 7 ?....Running CentOS Linux release 7.8.2003. Tom


Try running CentOS Linux 8.5.2111
3) Questions and Answers : Issue Discussion : Restriction based on GLIBC version? (Message 1452)
Posted 19 Dec 2021 by Dirk Broer
Post:
Why not upgrade from CentOS 7 to CentOS 8 or 9? CentOS 8 has GLIBC 2.28, CentOS 9 even has GLIBC 2.34.
4) Questions and Answers : Unix/Linux : GPU update (Message 1451)
Posted 19 Dec 2021 by Dirk Broer
Post:
Cuda on linux has never worked for me to date. But with 9.8, it is working now. Nice!


I have CUDA on Linux/ARM - but there are no apps

Jetson-Nano2GB
Starting BOINC client version 7.9.3 for aarch64-unknown-linux-gnu
log flags: file_xfer, sched_ops, task
Libraries: libcurl/7.58.0 OpenSSL/1.1.1 zlib/1.2.11 libidn2/2.0.4 libpsl/0.19.1 (+libidn2/2.0.4) nghttp2/1.30.0 librtmp/2.3
Data directory: /var/lib/boinc-client
CUDA: NVIDIA GPU 0: NVIDIA Tegra X1 (driver version unknown, CUDA version 10.2, compute capability 5.3, 1979MB, 1343MB available, 236 GFLOPS peak) 
OpenCL CPU: pthread-cortex-a57 (OpenCL driver vendor: The pocl project, driver version 1.1, device version OpenCL 1.2 pocl HSTR: pthread-aarch64-unknown-linux-gnu-GENERIC)
[libc detection] gathered: 2.27, Ubuntu GLIBC 2.27-3ubuntu1.4
Host name: Jetson-Nano2GB
Processor: 4 ARM ARMv8 Processor rev 1 (v8l) [Impl 0x41 Arch 8 Variant 0x1 Part 0xd07 Rev 1]
Processor features: fp asimd evtstrm aes pmull sha1 sha2 crc32
OS: Linux Ubuntu: Ubuntu 18.04.5 LTS [4.9.201-tegra|libc 2.27 (Ubuntu GLIBC 2.27-3ubuntu1.4)]
Memory: 1.93 GB physical, 4.97 GB virtual
Disk: 58.41 GB total, 12.05 GB free
5) Questions and Answers : Unix/Linux : GPU update (Message 1450)
Posted 19 Dec 2021 by Dirk Broer
Post:
Nvidia developer boards are nothing but low power ARM cores (A55, A53, A52 or lower) with a low power GPU that sits somewhere around a GT730. They're not the right boards to do compute calculations with. Especially their CPU compute numbers are very, very slow! They're expensive, and for the $99 a jetson nano costs, you'll probably rather buy a GT1030 (that's about twice to trice as fast). The Pi4B uses A72 cores, they're slightly better for compute loads.


For the most part this is plain bulls**t.
Nvidia developer boards have at least four Cortex-A57 cores (Jetson Nano 2gb and 4GB) of much higher performance than the quoted A55 and A53 cores -the A52 does not even exist The other boards have more and better cores (the Jetson TX1/TX2: Nvidia Denver, the Jetson Xavier NX/AGX: Nvidia Carmel and the Jetson Orin AGX a 12-core Cortex-A78.
The weakest GPU can be found on the quite affordable Nano's, that have a 128-core NVIDIA Maxwell architecture-based GPU. If you want to spend more money the Jetson Xavier NX already gives you a 384-core NVIDIA Volta GPU with 48 Tensor Cores -and the Xavier NX has six Nvidia Carmel CPU-cores that are superior to those of the Cortex-A72. I money is no objection, the 12-core Cortex-A78 Orin has a 2048-core Ampere architecture-based GPU with 64 Tensor Cores. Try to emulate that performance on your GT1030 -and fail utterly.
6) Questions and Answers : Unix/Linux : PI4, Error while computing (Message 1207)
Posted 30 May 2021 by Dirk Broer
Post:
Doing all the things I suggested does not even help you with the 32-bit Linux/ARM app on a 64-bit platform, as the 32-bit app seeks for GLIBC 2.28, while 64-bit Linux is at 2.27


-at least in the case of my Jetson Nano's.

My 64-bit Raspberry Pi 4 reports "error while loading shared libraries: libz.so.1"
7) Questions and Answers : Unix/Linux : PI4, Error while computing (Message 1206)
Posted 30 May 2021 by Dirk Broer
Post:
Doing all the things I suggested does not even help you with the 32-bit Linux/ARM app on a 64-bit platform, as the 32-bit app seeks for GLIBC 2.28, while 64-bit Linux is at 2.27
8) Questions and Answers : Unix/Linux : Pi4 (Message 1178)
Posted 26 Apr 2021 by Dirk Broer
Post:
Perhaps enlightening in the case of x86-64 Intel Atom vs ARM is the view offered on the page CPU performance of this project
The lowest five entries are for four ARM CPUs and the Intel Atom x5-Z8350, where the Atom is wedged between the Cortex-A53 (ARMv7 Processor rev 4 (v7l)[Impl 0x41 Arch 7 Variant 0x0 Part 0xd03 Rev 4]) of the Raspberry P 3+ and Cortex-A57 (ARMv8 Processor rev 1 (v8l)[Impl 0x41 Arch 8 Variant 0x1 Part 0xd07 Rev 1]) of the Nvidia Jetson Nano below and both the 32-bit (ARMv7 Processor rev 3 (v7l)[Impl 0x41 Arch 7 Variant 0x0 Part 0xd08 Rev 3]) and 64-bit (BCM2835[Impl 0x41 Arch 8 Variant 0x0 Part 0xd08 Rev 3]) running Cortex-72 of the Raspberry Pi 4 above.
9) Questions and Answers : Issue Discussion : More teams needed in team list (Message 1176)
Posted 26 Apr 2021 by Dirk Broer
Post:
Hello, I only see 20 teams to choose from in the team list. Please add all the other teams that other projects have listed. There are hundreds of active ones. Thank you.


The list of teams is made up by the team membership of the individual volunteers, as with each other project. So you are actually asking for more volunteers, especially from other teams, but there should be an option for the project to import boinc-wide teams though.
What would help in the past was a challenge on BOINCstats for a project, in order to gain more volunteers. With the new interface at BOINCstats however even my own team captain can't find out how to join a challenge anymore. Perhaps as a result of this, far less challenges are issued at BOINCstats nowadays -but it might also be a lack of interest from the teams themselves.
The alternative is e.g. FormulaBOINC or being chosen for the BOINC Pentathlon.

BTW: Note that you can also start a team on a project, either a new one or one for a team that you are a member of.
10) Questions and Answers : Unix/Linux : PI4, Error while computing (Message 1149)
Posted 16 Apr 2021 by Dirk Broer
Post:
Could it be that all 64-bit WUs run without problem, but that all 32-bit WUs fail?

do you have this in the cc_config.xml:
<cc_config>
<options>
<alt_platform>arm-unknown-linux-gnueabihf</alt_platform>
<alt_platform>armv7l-unknown-linux-gnueabihf</alt_platform>
</options>
</cc_config>


did you do this:
sudo dpkg --add-architecture armhf
sudo apt update --fix-missing
sudo apt dist-upgrade
sudo apt install libc6:armhf libstdc++6:armhf zlib1g:armhf libfuse2:armhf
11) Questions and Answers : Unix/Linux : Linux for Windows Users (Message 1127)
Posted 23 Mar 2021 by Dirk Broer
Post:
Just my two cents, but this project makes use of the CUDA libraries for the GPU version of the application. There is also a GPU test version using AMDROCM, but that uses a non-standard driver, one that doesn't get installed when installing whichever version of Linux, nor of the standard proprietary AMD GPUPRO drivers.
12) Questions and Answers : Unix/Linux : Pi4 (Message 872)
Posted 21 Nov 2020 by Dirk Broer
Post:
At this point, I'd say that the only ARM CPUs worth investing time over are the A70 cores with Neon instructions, as well as the Neonverse (which should have come out this year already, but hasn't).


Sorry to correct you yet again: it is Neoverse, not Neonverse. It has nothing to do with NEON instructions.
13) Questions and Answers : Unix/Linux : Pi4 (Message 857)
Posted 19 Nov 2020 by Dirk Broer
Post:
My personal tip for an ARM-based SBC to be used in this project would be the nVidia Jetson Nano (quad-core Cortex-A57 @1430 Mhz, stock), coupled to a good cooling fan so it can be overclocked to 2000 MHz.

You can augment the Jetson Nano using a CORAL USB Accelerator, so you can use not only the TensorFlow, PyTorch, Caffe en MXNet AI framework as can be used with the nVidia Jetson Nano, but also have a TPU-coprocessor in the form of the CORAL USB stick. That stick can also be used in other ARM SBC's such as the Raspberry Pi 4 or the Odroid-N2+ of course.


Apparently all Cortex A50 series CPUs are very, very slow, an not worth doing compute on.
That includes any of the Nvidia developer boards.
The only thing why they may be somewhat good, is their GPU capabilities.
But even those are equivalent of a GT730, which is half the speed of a 1030, which is half the speed of a 1050, which is half the speed of a 1660, which is half the speed of an RTX2060, which is about half the speed of an RTX 3080.
So if you're going to do GPU computations, a 3080 is about 40 to 60x faster as a GT730.


There are several misconceptions here, amongst them that ALL Cortex A50 series CPUs are very, very slow. Firstly, the oldest of them, the Cortex-A57 has more in common with the later A70 series than with the A53 and A55 and, secondly, the A57 can be run at 2000 MHz, far faster than the A53 of e.g. the Raspberry Pi 3.
Another misconception is that the Nvidia developer boards all use Cortex A50 SOCs. Only the Jetson Nano does, but the far better A57. The Jetson Xavier NX uses a 6-core NVIDIA Carmel comparable with the A70 series, as does the Jetson AGX Xavier -but then as an octa-core. The Nano has 128 Maxwell CUDA cores, but the Xavier NX has 384 Volta CUDA cores and the AGX Xavier has 512 Volta CUDA cores. These Volta cores are quite something different.

For ARM CPUs, you'll have to rely on Cortex A70 series CPUs.
The 72 is mostly only found in quad core configuration.
The higher end cores (eg: A77) are mostly found in Big-Little configuration, which android disables for program access due to device overheating. Meaning, if you want to crunch data on a big-little in android, the tasks will be shifted to the little cores.
Unless you can find a Linux image for the device, and make sure it has sufficient cooling.


Of course you need to cool an ARM device if you want to crunch on it. And when sufficiently cooled, all cores work, both big and LITTLE. -they do in Android on an Odroid-N2+ of me as well as in Linux on another Odroid-N2+. Both have a 80mm fan and a huge heatsink.

At this point, I'd say that the only ARM CPUs worth investing time over are the A70 cores with Neon instructions, as well as the Neonverse (which should have come out this year already, but hasn't).
These chips also can't be cellphones, tablets, or anything with a battery.
They have to be top boxes,or built into a server or desktop where sufficient cooling is present.
Think Pi4, which throttles unless there's either an active cooling, or a large heat sink cooling the CPU.
And that's only a cortex A72.
The higher end models are built on 12 and 10nm, but can reach over 3Ghz speeds.
No one makes them yet.


You can reach 2147 MHz presently on a Pi 4, if you use a good cooler. If you have spare parts from days past: use a Nortbridge cooler with heatpipes (e.g. a Noctua NC-U6) and a 80mm fan.
The Odroid-N2+ (with four Cortex-A73's and two A-53's) reaches 2400 MHz on the big cores when the fan is added, the LITTLE cores reach 2000 MHz.

NEON is a 32-bit ARMv7 instruction and useless for the 64-bit ARMv8 environment
14) Questions and Answers : Unix/Linux : Pi4 (Message 456)
Posted 8 Sep 2020 by Dirk Broer
Post:
My personal tip for an ARM-based SBC to be used in this project would be the nVidia Jetson Nano (quad-core Cortex-A57 @1430 Mhz, stock), coupled to a good cooling fan so it can be overclocked to 2000 MHz.

You can augment the Jetson Nano using a CORAL USB Accelerator, so you can use not only the TensorFlow, PyTorch, Caffe en MXNet AI framework as can be used with the nVidia Jetson Nano, but also have a TPU-coprocessor in the form of the CORAL USB stick. That stick can also be used in other ARM SBC's such as the Raspberry Pi 4 or the Odroid-N2+ of course.
15) Questions and Answers : Unix/Linux : Pi4 (Message 455)
Posted 7 Sep 2020 by Dirk Broer
Post:
The four cores of the Raspberry Pi 4 may be built on a 28nm process, they are Cortex-A72 cores and at that they beat the Amlogic S905X3 Cortex-A55 cores running @2000 MHz. They even beat the A55 cores running at their own rated clockspeed (1500 MHz), let alone when overclocked to 2000 MHz ExplainingComputers.com: Odroid C4 vs Raspberry Pi 4.

The Amlogic S922X as used in the Odroid-N2+ (and no doubt soon in TV boxes as well) is a better option than the Pi4, performance-wise: the four big Cotex-A73 cores can be overclocked to 2400 MHz, while the two LITTLE Cortex-A53 cores can run @2000 MHz (so the speed of the big vs LITTLE has changed from the original N2 model).
16) Questions and Answers : Unix/Linux : NPU and TPU AI Co-processors (Message 101)
Posted 4 Jul 2020 by Dirk Broer
Post:
But you will eventually have projects using nvidia gpus? Some of the older teslas like the m40 and the k80 are fairly cheap these days and have a lot of horsepower. Not to mention a lot of folks who run boinc are also gamers or devs and have some beefy gpus


If PyTorch can be used to accelerate computing for this project then it will automatically turn to Nvidia Tensor-capable GPUs -and perhaps to ARM-based Nvidia Jetson SBCs
17) Questions and Answers : Issue Discussion : No longer getting tasks (Message 86)
Posted 4 Jul 2020 by Dirk Broer
Post:
Funny, I had one system set to 'No More Tasks' (because I run PrimeGrid on that system).
When I enabled that system again, WUs started fooding -only to that system.
18) Questions and Answers : Issue Discussion : No longer getting tasks (Message 81)
Posted 4 Jul 2020 by Dirk Broer
Post:
Been crunching ok since day 1, now today, no new tasks being sent.. nothing has changed on my end on any of the 3 machines.... Sever shows 17k unsent.
Any ideas? Something up?

Enable test applications in your project preferences.


And when you've done that, and still nothing happens? Even more mysterious: when you check that setting, just to be sure, there is no longer such an option.....
19) Questions and Answers : Issue Discussion : Inclusion in BOINCstats (Message 80)
Posted 4 Jul 2020 by Dirk Broer
Post:
For an inclusion into BOINCstats the project admin should give his consent in this thread.
20) Questions and Answers : Unix/Linux : NPU and TPU AI Co-processors (Message 7)
Posted 1 Jul 2020 by Dirk Broer
Post:
Will this project support NPUs (Neural Processing Unit) and/or TPUs (Tensor Processing Unit), such as the ARM Ethos-N37/N57/N77, the Lightspeeur 2801S in the Orange Pi 4B or the Google Edge TPU in e.g. the Coral Dev Board, the Rock Pi N10 and the Asus Tinker Edge T and Tinker Edge R ?




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A project of the Cognition, Robotics, and Learning (CORAL) Lab at the University of Maryland, Baltimore County (UMBC)