Can CUDA use shared memory?

Can CUDA use shared memory?

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.

How do I declare a shared memory?

Declaring Shared Memory Shared memory is declared in the kernel using the __shared__ variable type qualifier. In this example, we declare an array in shared memory of size thread block since 1) shared memory is per-block memory, and 2) each thread only accesses an array element once.

Which of the following has access to shared memory in CUDA?

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.

Is shared memory per-block?

Each block has its own per-block shared memory, which is shared among the threads within that block.

Is shared memory faster?

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.

What is CUDA malloc?

cudaMalloc is a function that can be called from the host or the device to allocate memory on the device, much like malloc for the host. The memory allocated with cudaMalloc must be freed with cudaFree.

What is shared memory in GPU?

Shared memory represents system memory that can be used by the GPU. Shared memory can be used by the CPU when needed or as “video memory” for the GPU when needed. If you look under the details tab, there is a breakdown of GPU memory by process. This number represents the total amount of memory used by that process.

What is Cuda occupancy?

cuda. The occupancy is defined to be the number of active warps over the number of max warps supported on one Stream Multiprocessor. Let us say I have 4 blocks running on one SM, each block has 320 threads, i.e., 10 warps, so 40 warps on one SM. The Occupancy is 40/48, assuming max warps on one SM is 48 (CC 2. x).

Why is shared memory so fast?

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 faster than pipe?

2 Answers. Once Shared Memory is setup by the kernel there is no further need of kernel for the communication b/w process whereas in Pipe, data is buffered in the kernel space and requires system call for each access. Here, Shared Memory is faster than Pipe.

What are the advantages of CUDA?


  • Scattered reads – code can read from arbitrary addresses in memory.
  • Unified virtual memory (CUDA 4.0 and above)
  • Unified memory (CUDA 6.0 and above)
  • Shared memory – CUDA exposes a fast shared memory region that can be shared among threads.
  • Faster downloads and readbacks to and from the GPU.