I wrote a program with xcode (using portaudio) on a MacBook Pro (Intel Core 2 Duo 2.66 GHz). The Release works without problem (clear audio streaming) and the CPU Usage Level is almost 90%.
The problem arises when i run the Release on a Mac Pro (Quad Core Intel Xeon 2.8 GHz). The audio stream, when there is a large amount of computation, isn't clear (there are little clicks) despite the use of the CPU is four times lower than the one of MacBook Pro.
I can not understand why this happens.
25% CPU usage in a 4 core system means one core is 100% loaded. Also, I assume the Xeons are Pentium4 Xeons, which have way worse CPU cores than the Core2Duo, even though the clock frequency is a bit higher...
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I have a 2019 MacBook Pro 16". It has an Intel Core i9, 8-core processor and an AMD Radeon Pro 5500M with 8 GB GPU RAM.
I have the laptop dual booting Mac OS 12.4 and Windows 11.
Running clinfo under Windows tells me essentially that the OpenCL support is version 2.0, and that the addressing is 64-bits, and the max allocatable memory is between 7-8 GB.
Running clinfo under Mac OS tells me that OpenCL support is version 1.2, that addressing is 32-bits little endian, and the max allocatable memory is about 2 GB.
I am guessing this means that any OpenCL code I run is then restricted to using 2GB because of the 32-bit addressing (I thought that limit was 4GB), but I am wondering a) is this true and b) if it is true, is there any way to enable OpenCL under Mac to use the full amount of GPU memory?
OpenCL support on macOS is not great and has not been updated/improved for almost a decade. It always maxes out at version 1.2 regardless of hardware.
I'm not sure how clinfo determines "max allocatable memory," but if this refers to CL_DEVICE_MAX_MEM_ALLOC_SIZE, this is not necessarily a hard limit and can be overly conservative at times. 32-bit addressing may introduce a hard limit though. I'd also experiment with allocating your memory as multiple buffers rather than one giant one.
For serious GPU programming on macOS, it's hard to recommend OpenCL these days - tooling and feature support on Apple's own Metal API is much better, but of course not source compatible with OpenCL and only available on Apple's own platforms. (OpenCL is now also explicitly deprecated on macOS.)
Fellow colleagues,
I'm currently working on a PowerPC emulator written in C++. In order to evaluate its performance, I'm using std::chrono:high_resolution_clock to measure execution time of a guest code block for that the number of CPU cycles is known. The corresponding code is located here: https://github.com/maximumspatium/dingusppc/commit/11b4e99376e23f46f4cd8ee6223c5788ab963a37
While doing the above tests, I noticed that my MacBook Pro reports different numbers depending on CPU load. That is, when I run the above code with idle CPU I'm getting about 230000 ns execution time while with heavy-loaded CPU (neural net training, for example) I'm getting much better performance (< 70000 ns).
I suppose it's related to threads and scheduling in macOS. I'd like to utilize the full CPU power in my emulator. Is there a way to change thread performance to run at full speed, just like it does when the CPU is running under heavy load?
Thanks in advance!
P.S.: The machine in question is MacBook Pro 17ยดยด Mid 2010 with 2,53 GHz Intel Core i5 and 8GB RAM running MacOS 10.13.6.
I have a MBP with 16GB of RAM. As projects grow in Xcode, the compile time does take longer. I'm looking into starting a hackintosh project purely for shortening Xcode compilation time. Since RAM is cheap, I wanna push the normal boundaries. But the biggest question is will Xcode be capable of using all the RAM greater than 32GB? I know there will be some diminishing marginal returns at some point of RAM increase.
RAM usage is mostly governed by the OS, because the Mac Pro does support up to 64GB of RAM, so should OSX (and by extension XCode).
Although I wonder if your compile time issues are actually RAM-related. I have Xcode projects that take minutes to build and it's all because my CPU is pegged at 100% (using a mid-2015 15" retina MBP). Not many software projects are RAM-constrained past 16GB.
Curious if anyone out there is doing Android Studio development on a dual Xeon machine.
I would like to know if the additional CPU gave a dramatic or visible (50% or more) boost in build performance.
You probably found out, but for others wondering: Chances are - it won't.
Did some testing with two relatively quick E2650 v4 Xeons on a largish Java + Kotlin project and Xeons were considerably slower than low core count / higher clock CPU's.
Check out the benchmarks here:
https://superuser.com/questions/1115206/will-dual-xeons-improve-android-studio-build-times/
I have tried to measure speed of Android Studio 3.1.4 on the same hardware: Macbook Pro 2011, RAM 4Gb, SSD 240GB Samsung, Core i5 2.4Ghz.
I have installed on this machine 3 different OS: Windows 10, MacOS Hight Sierra 10.13, Ubuntu 18.04.
Avarage build time (running command: gradlew clean build, gradlew clean assembleRelease) on MacOS/Ubuntu was around 30% faster than on Windows.
On my another working machine: Core i5 3.0 Ghz 7400, RAM 16Gb, SSD 250Gb. Build time takes 4.34min on Windows 10 machine.
The same project on a little bit slower processor, but with the same RAM and SSD and it is running Ubuntu 16.04 build time takes two times faster!!
Well I was shocked with results, but still I choose Windows as development machine, because it's much more comfortable for me to use comfortable and
usable keyboard and sotfware than on Unix like systems. And even if I had to choose between MacOS and Ubuntu - mac is really much easier to setup everything, and
Ubuntu is too complex to use for usual people. Choise is up to you.
I have a Mac OS X with 8 GB 1600 MHz DDR3 RAM and when I start unreal engine it complaints about Low RAM and asks that for best performance I should have at least 8 GB of RAM.
I'm not sure why is this is case, is it possible that its getting a lower share of RAM or something similar?
The Unreal Editor version is 4.7.5
Edit: The processor is 2.3 GHz Intel Core i7, and is 64 bit.
Edit: The graphic processor is Intel Iris Pro 1024 MB.
Why would UE4 not be able to access all of your RAM? I do not consider that case very likely.
Also, I would not worry about the RAM usage of UE4 too much. I do a lot of work with the engine and it rarely uses more than 3GB of RAM. Just make sure that your system as a whole has enough RAM for the running processes to prevent swapping.
Anyway, the bottleneck in your system is probably the graphics processor, so if your engine runs too slowly you should reduce the performance setting inside the engine.