I have recently purchased a development board utilizing Samsung Exynos5422 application processor (Cortex™-A15 2.0Ghz quad core and Cortex™-A7 quad core CPUs). I have tried to extract the performance counters in android using perf v3.0.8; however, none of the counters outputs a value (They are all "not counted"). Does anyone know how to solve this issue?
(The kernel version is 3.10.9)
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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.)
I have had created VM instances using google cloud platform (using console). The VM is a based on WINDOWS SERVER 2019! I have been successful in making one but unable to get some virtual ram in the instances. It (VRAM) shows zero. Does adding GPU not increase the vram? If not then what increases them? I am looking to increase the same for gaming purposes and using software like ADOBE AND AUTODESK too...
Instances created with additional GPU's (Like Tesla K80 and other) have all specified amount of GPU memory (VRAM).
You can find list of all GPU's in the documentation.
Every GPU has an amount of memory specified in the table.
If you create a VM with one K80 GPU it will kave 16GB of DDR6 memory available (nothing to do with the type of the machine or actual RAM assigned).
You can find how much of VRAM a GPU has in the Device manager; find "Display adapters" and expand it and find your card; it's all in the "general" tab.
And regarding any Adobe or Autodesk software I can't really tell if having Tesla will be of advantage..
As I was reading through the kernel source code I noticed that a mapping between the physical core id and the virtual core number is being created. This could be because there is some degree of uncertainty in the order in which the cores are brought up.
In a multi-core system, which physical core is the first to boot? Is it always physical core #0? Does this hold for x86, x64, ARM and ARM64?
According to the Intel SDM, in recent Intel processors the selection of the bootstrap processor (BSP) is handled either "through a special system bus cycle" or "by platform-specific arrangement of the combination of hardware, BIOS, and/or configuration input options."
In my experience (with Intel processors only), the BSP always has APIC ID 0 (although this is not guaranteed). However, I don't know whether that means that it is always the same physical core within the processor, or even if there is any way to tell.
For more information, see section 8.4 of the Intel SDM, volume 3A.
my laptop is i7 4510U , 4GB ram , Intel HD graphics 2gb , Nvidia GeForce 840m 4GB
why it's so laggy when Iam using android studio?
Your 4GB of ram is to blame,
to use well Android Studio, you need at least 8GB of RAM (more if intend to use emulators), just note that the GPU have no use here, and even the processor you just need a mid range processor to work well, but when compiling the more powerful processor you have the better.
I also had problems but then i disabled some plugins and tried to build gradle offline, you can find detailed answer here :
http://stackoverflow.com/questions/30817871/android-studio-is-slow-how-to-speed-up
When building a project, I can see Xcode working on 4 Swift files at the same time in the Build Log inside the report navigator.
I also see that there are 4 processes in Activity Manager, all called "Swift", when it's compiling.
It seems to be doing a great use of the available processing power to do it's job, so I'm not looking to change that. However, i'm working with a Dual Core i7 processor.
Would a Quad Core i7 be able to compile 8 files in parallel? And if so, would it also scale up if it were 6 cores?
Could someone with at least a Quad Core do a test to confirm this?
I'm using Xcode 6.3.1
Thank you!
Xcode will use as many available processors as it can. There are some situations where it cannot use all processors, for example it cannot compile Objective-C files until all precompiled header files are compiled, so if you have one precompiled header file only, then only one processor will be used for that. But for all your normal source files, all the processors will be used. And for the static analyzer, all the processors will be used as well.
In other words, the quad core Retina MBP 15" is a very fine development machine. Give it lots of RAM if you have large source files, I had problems with 8 core MacPro with 4GB of RAM (long time ago). An older Mac Mini with four cores is also quite useful.
This is the CPU Usage on my MacBook Pro with the Apple M1 Pro during the normal thinking-typing-thinking-typing work that takes up 99% of my work day. The two cores on the left are the efficiency cores and the 8 on the right are the performance cores. It spends most of its time just like that. And there are a dozen apps open.
And when it’s time to see if that thinking-typing amounted to anything it’s nice to see all 10 cores max out while the app builds. That’s cool.