Practice CUDA core
A NVIDIA GPUs contains 1-N Streaming Multiprocessors (SM). Each SM has 1-4 warp schedulers. Each warp scheduler has a register file and multiple execution units. The execution units may be exclusive to the warp scheduler or shared between schedulers. Execution units include CUDA cores (FP/INT), special function units, texture, and load store units. The Fermi and Kepler white papers provide additional information.
”queue of blocks” associated with each kernel launch.
As resources on a SM become available, the block scheduler will deposit a block from the ”queue” onto that SM. Block scheduler does not deposit blocks warp-by-warp. It is an all-or-nothing proposition, on a block-by-block basis.
Let us consider a block that is already deposited on a SM. A warp is ”eligible” when it has one or more instructions that are ready to be executed.
Ampere GPU is having maximum number of concurrent warps per SM which remains the same as in Volta (i.e., 64),’ The high-priority recommendations from those guides are as follows
Devices with the same major revision number are of the same core architecture.
CUDA version ( is sw version) versus CUDA Cores
The compute capability version of a particular GPU should not be confused with the CUDA version (for example, CUDA 7.5, CUDA 8, CUDA 9), which is the version of the CUDA software platform.
The CUDA platform is used by application developers to create applications that run on many generations of GPU architectures, including future GPU architectures yet to be invented. While new versions of the CUDA platform often add native support for a new GPU archiecture by supporting the compute capability version of that architecture, new versions of the CUDA platform typically also include
Software features that are independent of hardware generation.The multiprocessor creates, manages, schedules, and executes threads in groups of 32 parallel threads called warps When a multiprocessor is given one or more thread blocks to execute, it partitions them into warps and each warp gets scheduled by a warp scheduler for execution
The way a block is partitioned into warps is always the same; A warp executes one common instruction at a time, so full efficiency is realized when all 32 threads of a warp agree on their execution path.
The SIMT architecture is akin to SIMD (Single Instruction, Multi-ple Data) vector organizations in that a single instruction controlsmultiple processing element
cu code and python code for GPU device