What is workgroup in OpenCL?
Use of the work-groups allows more optimization for the kernel compilers. This is because data is not transferred between work-groups. Depending on used OpenCL device, there might be caches that can be used for local variables to result faster data accesses.
What is a compute unit OpenCL?
On the CUDA architecture, an OpenCL compute unit is the equivalent of a multiprocessor (which can have either 8, 32 or 48 cores at the time of writing), and these are designed to be able to simultaneously run up to 8 work groups (blocks in CUDA) each.
What is compute unit?
Compute units are similar to host groups, with the added feature of granularity allowing the construction of clusterwide structures that mimic network architecture.
What is compute units How does it work?
A compute unit (CU) is the unit of measurement for the resources consumed by actor runs and builds. We charge you for using actors based on CU consumption. For example, if you run an actor with 1GB of allocated memory for 1 hour, it will consume 1 CU.
What is a workgroup in GPU?
Workgroup n. Group of threads that are on the GPU at the same time. Also on the same compute unit.
What is workgroup size?
The Global work group size is frequently related to the problem size. The Local Work Group Size is selected based on maximizing Compute Unit throughput and the number of threads that need to share Local Memory. Let consider a couple of examples; A) Scale a image from N by M to X by Y.
What is CL kernel?
A kernel is essentially a function written in the OpenCL language that enables it to be compiled for execution on any device that supports OpenCL. The kernel is the only way the host can call a function that will run on a device. When the host invokes a kernel, many work items start running on the device.
What are GPU CUs?
A GPU compute device has multiple compute units. Compute units (CUs) is the term agreed to by the community for the OpenCL standard. Nvidia calls these streaming multiprocessors (SMs) and Intel refers to them as subslices.
What is a compute unit in GPU?
A GPU Platform can have one or more Devices. A GPU Device is organized as a grid of Compute Units. Each Compute Unit is organized as a grid of Processing Elements. So in NVIDIA terms, their new V100 GPU has 84 Compute Units, each of which has 64 Processing Elements, for a grand total of 5,396 Processing Elements.
Is a compute unit the same as a core?
What’s the Difference Between Compute Units and CUDA Cores? The main difference between a Compute Unit and a CUDA core is that the former refers to a core cluster, and the latter refers to a processing element.
What does the function cudaMalloc do?
Definition. 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 GPU wavefront?
It is the minimum size of the data processed in SIMD fashion, the smallest executable unit of code, and the way to processes a single instruction over all of the threads in it at the same time. In all GCN GPUs, a “wavefront” consists of 64 threads, and in all Nvidia GPUs, a “warp” consists of 32 threads.
What are GPU execution units?
Execution Unit An Intel GPU consists of a set of execution units (EU). Each EU is simultaneously multithreaded (SMT) with seven threads. The primary computation units are a pair of Single Instruction Multiple Data (SIMD) Arithmetic Logic Units (ALU).
What is the difference between GPU and GPGPU?
The primary difference is that where GPU computing is a hardware component, GPGPU is fundamentally a software concept in which specialized programming and equipment designs facilitate massive parallel processing of non-specialized calculations.
What is the difference between CUDA cores and tensor cores?
Tensor cores can compute a lot faster than the CUDA cores. CUDA cores perform one operation per clock cycle, whereas tensor cores can perform multiple operations per clock cycle. Everything comes with a cost, and here, the cost is accuracy. Accuracy takes a hit to boost the computation speed.
What is GPU allocation?
The GPU execution model in its rudimentary form follows an accelerator approach, in which the programmer has to explicitly allocate GPU memory and manage transfers between the CPU and the GPU, as well submitting code to be executed in the GPU, known as kernel.
What is host and device in GPU?
CUDA Programming Model Basics In CUDA, the host refers to the CPU and its memory, while the device refers to the GPU and its memory. Code run on the host can manage memory on both the host and device, and also launches kernels which are functions executed on the device.
What is a GPU architecture?
This is known as “heterogeneous” or “hybrid” computing. A CPU consists of four to eight CPU cores, while the GPU consists of hundreds of smaller cores. Together, they operate to crunch through the data in the application. This massively parallel architecture is what gives the GPU its high compute performance.