Can CUDA use shared memory?
- 1 Can CUDA use shared memory?
- 2 Which of the following has access to shared memory in CUDA?
- 3 What is CUDA in deep learning?
- 4 Why is shared memory so fast?
- 5 Are there any misaligned data accesses in CUDA?
Shared memory is a powerful feature for writing well optimized CUDA code. Access to shared memory is much faster than global memory access because it is located on chip. Because shared memory is shared by threads in a thread block, it provides a mechanism for threads to cooperate.
All threads of a block can access its shared memory. Shared memory can be used for inter-thread communication. Each block has its own shared-memory. Just like registers, shared memory is also on-chip, but they differ significantly in functionality and the respective access cost.
Is it beneficial to add more CUDA threads to an application?
To add to this, having more threads than cores in flight on an SM unit allows the GPU to hide memory access latency. If you have exactly as many threads running as you have cores, then when a thread accesses global memory it has to wait several hundred clock cycles before the data is actually received.
What is __ global __ In CUDA?
__global__ is a CUDA C keyword (declaration specifier) which says that the function, Executes on device (GPU) Calls from host (CPU) code.
Shared memory is the fastest form of interprocess communication. The main advantage of shared memory is that the copying of message data is eliminated. The usual mechanism for synchronizing shared memory access is semaphores.
Why is shared memory useful?
In computer science, shared memory is memory that may be simultaneously accessed by multiple programs with an intent to provide communication among them or avoid redundant copies. Shared memory is an efficient means of passing data between programs.
What is CUDA in deep learning?
CUDA is a programming model and a platform for parallel computing that was created by NVIDIA. CUDA programming was designed for computing with NVIDIA’s graphics processing units (GPUs).
Why is shared memory the fastest form of IPC? Once the memory is mapped into the address space of the processes that are sharing the memory region, processes do not execute any system calls into the kernel in passing data between processes, which would otherwise be required.
Why is shared memory slow?
2 Answers. If you are only using the data once and there is no data reuse between different threads in a block, then using shared memory will actually be slower. The reason is that when you copy data from global memory to shared, it still counts as a global transaction.
These situations are where in CUDA shared memory offers a solution. With the use of shared memory we can fetch data from global memory and place it into on-chip memory with far lower latency and higher bandwidth then global memory.
Declare shared memory in CUDA Fortran using the shared variable qualifier in device code. There are multiple ways to declare shared memory inside a kernel, depending on whether the amount of memory is known at compile time or at run time. The following complete code illustrates various methods of using shared memory.
How is global memory coalescing used in CUDA?
One way to use shared memory that leverages such thread cooperation is to enable global memory coalescing, as demonstrated by the array reversal in this post. By reversing the array using shared memory we are able to have all global memory reads and writes performed with unit stride, achieving full coalescing on any CUDA GPU.
Are there any misaligned data accesses in CUDA?
For recent versions of CUDA hardware, misaligned data accesses are not a big issue. However, striding through global memory is problematic regardless of the generation of the CUDA hardware, and would seem to be unavoidable in many cases, such as when accessing elements in a multidimensional array along the second and higher dimensions.