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.
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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.)
Planning on getting an 8gb ram laptop, at least 256gb ssd, with core i5(at least 8th gen) with financial management, I have seen post of some people saying 8gb ram is not enough to run intelliJ IDEA, some even say 16gb is just manageable and as I only intend to run just the ide before moving to android studio for building apps though I intend to get an android device for emulation alongside the android studio so I dont in any case use the virtual device, what do you guys suggest about getting the 8gb ram laptop, do you guys think the ssd can speed up my compilation without lagging or I should try to get a higher ram. Besides, I have a Lenovo thinkpad X140e mini laptop with 3gb ram, amd A4 series chip, 256gb HDD, and the laptop is so slow as it takes about 2 minutes to compile a simple println statement and after compiling about 5 codes, it might just freeze for more than 6 minutes, need help from experienced users and the time it takes to compile a code in the ide (those with the 8gb ram laptop and ssd)
I'm running windows 10 with 4GBs of DDR3 1066 on Intel second generation i5 mobile architecture.
I come from a OSX background mostly and memory has always been a concern for me because I prefer to have many tabs open. I noticed on OSX that the memory usage didn't relate that much to the performance of the applications so long as it wasn't fully saturated but easily on my iMac I can run 80% of memory and find no noticeable lag or stuttering. However on Windows I'm finding memory to be the major bottleneck in my system, I understand that upgrading to 8 or 16GBs of memory would be the upgrade path for me. However I would love to understand why my system slows down noticeably when I saturate 80% of the memory unlike OSX that seems to handle it just fine. Is it a bandwidth limitation? I know that Windows NT and Darwin are completely different Kernels and I would love to be educated in exactly how that affects the same usage scenario so differently.
Thank you in advance.
My development machine has a 128G SSD. I recently upgraded to VS2013 Ultimate, and found that it ate 7.3G of it. In comparison, my VS2010 takes up just over 500M.
I cannot imagine why this is so much larger, and suspect I have a bunch of stuff on here I don't need. Does anyone have any way to characterize where that space is going, and what I might be able to do to get some of it back?
No, you cannot.
Per the Microsoft website, VS 2013 requires 20GB.
Hardware requirements
1.6 GHz or faster processor
1 GB of RAM (1.5 GB if running on a virtual machine)
20 GB of available hard disk space
5400 RPM hard disk drive
DirectX 9-capable video card that runs at 1024 x 768 or higher
display resolution
I am surprised that you're around 7GB
Maybe you should look at VS2013 Online Advanced, if you're really interested in HD space:
Hardware requirements
1.6 GHz or faster processor
1 GB of RAM (1.5 GB if running on a virtual machine)
5 GB of available hard disk space
5400 RPM hard drive
DirectX 9-capable video card running at 1024 x 768 or higher display
resolution
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My original question is that, is this technically rational to check the required heap-size of my Java program on Windows 7, via VisualVM, and come to this conclusion that the program will require the same amount of heap on Linux(RedHat) as well?
I don't know how the system(OS or even CPU and RAM), affect memory management of JVM.
well, the windows is my development system with 4GB of RAM and a Core 2 Due CPU, however the
Linux is the production system with 32GB of RAM and multiple powerfull processors,
Actually, my concern is that the program on Linux might need more memory. less is ok.