I am looking to upgrade an older machine we have at our lab to use for deep learning (PyTorch) in addition to my personal work station. Its an older Dell work station but the relevant specs are as follows:
PSU: 950W
RAM: 64 GB DDR4 ECC
CPU: Xeon Bronze 3104 #1.7 GHz
It even has an older NVIDIA GPU I can use for display output when the A4000 is fully loaded like I currently do on my personal setup.
Through the university we can acquire a RTX A4000 (I know not best price to performance), which is basically a 3070ti with more VRAM. I am concerned that the low clock speeds may cause a bottle neck. Does anyone have experience with a similar configuration?
Thank you for the help!
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I recently bought a dell XPS 15 with a i9 12900 HK, and downloaded Ubuntu 22.04 LTS as an OS.
I coded a simple OpenMP program, that should have a linear speedup in the number of threads (the code is correct because it behaves as expected when I run it on a cluster), but on my laptop, it stops the speedup at 6 threads, even if my processor has 20 threads. I made some research, and read that kernel 5.15 is not optimised for last Intel processors because it makes a bad use of P and E cores.
But I also read that it may be dangerous to update the kernel to a newer version like 5.17 or 5.18 because my GTX 3050 Ti drivers may not be compatible with this kernel.
Can I update the kernel safely ? Will it resolves my parallelism problem? What method should I use to update my kernel?
I tried to look to forums and docs, but lots of available documentation are from third parties and I don't know if I can trust them.
I have developed a macOS app which is heavily relying on multithreading (a call center simulator). It runs fine on my iMac 2019 and fills up all cores nicely. In my test scenario it simulates app. 1.4 mio. telephone calls in total in 100 iterations, each iteration as a dispatch item on a parallel dispatch queue.
Now I have bought a new Mac mini with M1 Apple Silicon and I was eager to see how the performance develops on that test machine. Well, it’s not bad but not as good as I expected:
System
Duration
iMac 2019, Intel 6-core i5, 3.0 GHz, Catalina macOS 10.15.7
19.95 s
Mac mini, M1 8-core, Big Sur macOS 11.2, Rosetta2
26.85 s
Mac mini, M1 8-core, Big Sur macOS 11.2, native ARM
17.07 s
Investigating a little bit further I noticed that at the start of the simulation all 8 cores of the M1 Mac are filled up properly but after a few seconds only the 4 high efficiency cores are used any more.
I have read the Apple docs „Optimize for Apple Silicon with performance and efficiency cores“ and double checked that the dispatch queue for the iterations is set up properly:
let simQueue = DispatchQueue.global(qos: .userInitiated)
But no success. After a few seconds of running the high performance cores are obviously not utilized any more. I even tried to set up the queue with qos set to .userInteracive up that didn’t help either. I also flagged the dispatch items with proper qos but that didn’t change anything. It looks to me that other apps (e.g. XCode) do utilize the high performance cores even for a longer time.
Does anybody know how to force a M1 Mac to utilize the high performance cores?
"M1 8 core" is really "M1 4 performance + 4 power saving cores". I expect it to have be a bit more performance than an Intel 6 core, but not much. Exactly has you see, 15% faster than six Intel cores or about as fast as 7 Intel cores would be. The current M1 chips are low end processors. "A bit better than Intel six cores" is quite good.
Your code must be running on the performance cores, otherwise there would be no chance at all to come close to the Intel performance. In that graph, nothing tells you which cores are used.
What happens most likely is that all cores start running, each trying to do one eighth of the work, and after about 8 seconds the performance cores have their work done. Then the power saving cores move their work to the performance cores. And you are just misinterpreting the image as only low performance cores doing the work.
I would guess that Apple has put a preference on using efficiency cores over performance for many reasons. Battery life being one, and most likely thermal reasons as well. This is the big question mark with a SoC that originally was designed for smartphones and tablets. MacOS is a much heavier OS then IOS or iPad OS. Apple most likely felt that performance cores were only needed in the cases where maximum throughput was needed. No doubt, I think some including myself with a M1 Mac Mini would like a way to adjust this balance between efficiency and performance cores. Personally overall, I would prefer all cores be capable of switching between efficiency and performance such as in Intel's Speed shift technology. This may come along with the M1's advancements in terms of Mac Pro models and other Pro models.
I am not really familiar with shared clusters, but I am assuming performance should not differ much in terms of completing a single task when compared with a laptop processor. I have a C++ code which I ran on my laptop with Intel(R)Core™ i7-4558U 2.80 GHz CPU and 16.0 GB RAM, with the operating system of 64 bit Windows 10. On the other hand, I have results of the same code from a publication which belong to the tests conducted on a shared cluster with Intel Xeon 2.3 GHz CPU and 4 GB memory limit with Linux operating system. The program uses CPLEX as the solver: my laptop has IBM Cplex 12.7 whereas previous runs used IBM CPLEX 12.4 (Cplex, 2012). My results seem to take 300 times more than the reported results of the previous run. Does this much difference make sense? If so what could be the driver behind it?
This could be attributed to performance variability (see, for example, section 5 of the MIPLIB 2010 paper here). In a nutshell, minor differences in problem formulation (e.g., order of constraints, input format, etc.), or running on different platforms, can have a great effect on the time to solve. With CPLEX 12.7, you can use the interactive to help you evaluate variability.
I'm attempting my first electron App. Does anyone know why i get this error AVDCreateGPUAccelerator: Error loading GPU renderer in the terminal when i do npm start? It doesn't seem to effect anything yet. I've gotten it right from the beginning of my install using the quick start guide. Will it effect me down the road?
mac pro late 2013
2.7 GHz 12-Core Intel Xeon E5
128 GB 800 MHz DDR3 ECC
AMD FirePro D700 6144 MB
Most likely the AVDCreateGPUAccelerator error is because the Xeon CPUs used in the Mac Pros do not have a hardware decoder. Core i3/5/7 CPUs in Mac mini/iMac/Mac Book Pro) should work fine.
For a solution to this, please refer to the following answer;
https://stackoverflow.com/a/57405820/10868666
I have an nvidia GTX 750 Ti card, which is advertised as having 640 CUDA cores. Indeed, the nvidia settings application also reports this.
I'm trying to use this card to do OpenCL development on Linux. Now, I have reported from the OpenCL environment (through PyOpenCL if it makes a difference) that the number of compute units is 5. My understanding is that one compute unit on an nvidia device maps to one multiprocessor, which I understand to be 32 SIMD units (which I assume is the a CUDA core).
Clearly, 5 * 32 is not 640 (rather a quarter of what is expected).
Am I missing something as regards the meaning of a a work unit on nvidia? The card is also driving the graphics output which will be using some of the computational capability - is a proportion of the processing capability reserved for graphics use? (if so, can I change this?).
NVIDIA have a whitepaper for the NVIDIA GeForce GTX 750 Ti, which is worth a read.
An OpenCL compute unit translates to a streaming multiprocessor in NVIDIA GPU terms. Each Maxwell SMM in your GPU contains 128 processing elements ("CUDA cores") - and 128*5 = 640. The SIMD width of the device is still 32, but each compute unit (SMM) can issue instructions to four different warps at once.