I have a doubt regarding memory allocation in VXWorks.
It looks like VxWorks allocates a few bytes more than requested
Scenario 1:
I request for 64 bytes. Vxworks allocates 66 bytes. Diff of 2 bytes
Scenario 2:
I request for memory of 88 bytes. Vxworks allcoates 96 bytes.Diff of 8 bytes.
Related
Hello All I hope you are fine , Please i have 2 Questions
if the ISA is 16 bits and Ram is 8 bit only for one address what will happen?
If My ram is 16 bits and ISA 16 bits the featch will get 16 bits and proccess 16 bits int the proccessor?
Thank you
Loading 1500 images of size (1000,1000,3) breaks the code and throughs kill 9 without any further error. Memory used before this line of code is 16% of system total memory. Total size of images direcotry is 7.1G.
X = np.asarray(images).astype('float64')
y = np.asarray(labels).astype('float64')
system spec is:
OS: macOS Catalina
processor: 2.2 GHz 6-Core Intel Core i7 16 GB 2
memory: 16 GB 2400 MHz DDR4
Update:
getting the bellow error while running the code on 32 vCPUs, 120 GB memory.
MemoryError: Unable to allocate 14.1 GiB for an array with shape (1200, 1024, 1024, 3) and data type float32
You would have to provide some more info/details for an exact answer but, assuming that this is a memory error(incredibly likely, size of the images on disk does not represent the size they would occupy in memory, so that is irrelevant. In 100% of all cases, the images in memory will occupy a lot more space due to pointers, objects that are needed and so on. Intuitively I would say that 16GB of ram is nowhere nearly enough to load 7GB of images. It's impossible to tell you how much you would need but from experience I would say that you'd need to bump it up to 64GB. If you are using Keras, I would suggest looking into the DirectoryIterator.
Edit:
As Cris Luengo pointed out, I missed the fact that you stated the size of the images.
ppData points to a pointer in which is returned a host-accessible
pointer to the beginning of the mapped range. This pointer minus
offset must be aligned to at least
VkPhysicalDeviceLimits::minMemoryMapAlignment.
I want to allocate a Vec3 float in a uniform buffer. A Vec3 float is 12bytes big.
VkMemoryRequirements { size: 16, alignment: 16, memory_type_bits: 15 }
Vulkan reports that it has to be aligned to 16 bytes, which means that the size of the allocation is now 16 instead of 12. So Vulkan already handled this for me.
minMemoryMapAlignment on my GPU is 64 bytes. What exactly does this mean for my allocation? Does this mean that I can not use the size from a VkMemoryRequirements for my allocation? And instead of allocating 16bytes here, I would have to allocate 64bytes?
Update:
For a 12 byte allocation with a 16 byte alignment and 64 bytes minMemoryMapAlignment. I would still allocate only 16 bytes and then call:
vkMapMemory(device, memory, 0, 16, 0, &mapped);
But the ptr returned from vkMapMemory is actually not 16 bytes but 64 bytes wide? And all the relevant data is in the first 12 bytes and the rest is just "padded" memory? So in practice this basically means that I don't need to use minMemoryMapAlignment at all?
There is nothing in the spec that restricts the size of the allocation like that. The paragraph you quoted means that the mapping will be aligned to minMemoryMapAlignment and you can then tell the compiler to use aligned memory accesses when accessing it. What will happen is that when the memory is mapped the later 48 bytes are wasted space in the host's memory space. That is unlikely to matter though.
This is why people keep saying to allocate larger blocks and subdivide them as needed. That way you can put 4 of those vkBuffers into a single 64 byte allocation (which you will need if you want to pipeline the rendering).
It's highly unlikely that that single vec3 is the only thing you need memory for, so take a look at your other allocations and see which ones you can combine.
When we use spark on yarn for non-streaming apps, we generally get the allocated memory to match the number of executors times memory per executor. When doing streaming apps, the allocated memory is immediately pushed to the limit (total memory) as shown in the yarn console.
With this set of parameters
--driver-memory 2g --num-executors 32 --executor-memory 500m
total memory 90G, memory used 85.88G
total vcores 64, vcores used 33
you would expect a basis of 32 * 1 G (500m + overhead) + driver memory or around 34 G, and 33 vcores (32 workers + 1 driver)
question:
is the 64 vcore due to the requirement of 2 core pairs for streaming connection and processing?
how did the estimated 34 G get pushed to 85.88 G? is this always true that with streaming apps, yarn gives it all it has?
If a program running on a little endian processor writes the value 0xaabbccdd uncached to address 0, and the processor uses a 32-bit wide AXI4 bus, are bits 31-24 of WDATA 0xaa or 0xdd?
AXI does not expose byte addressable memory--it can only read or write a full data bus width (32 bits in this case). The question is how it maps addresses of byte values to data bus bits.
Section A3.4.3 of the AXI spec (rev E) discusses "byte invariant" endianness, but doesn't seem to explain the order of bytes on the data bus.
If a little endian processor writes the value 0xaabbccdd uncached to address 0 via a 32-bit wide AXI4 bus, are bits 31-24 of WDATA 0xaa or 0xdd?
31-24 bits of WDATA are 0xAA independent of processor endianness. But the order of bytes in memory is determined by the endianness. See this answer for explanation of byte-invariant endianness.