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CuPy is a part of the NumPy ecosystem array libraries [7] and is widely adopted to utilize GPU with Python, [8] especially in high-performance computing environments such as Summit, [9] Perlmutter, [10] EULER, [11] and ABCI.
CUDA provides both a low level API (CUDA Driver API, non single-source) and a higher level API (CUDA Runtime API, single-source). The initial CUDA SDK was made public on 15 February 2007, for Microsoft Windows and Linux. Mac OS X support was later added in version 2.0, [18] which supersedes the beta released February 14, 2008. [19]
The setp.cc.type instruction sets a predicate register to the result of comparing two registers of appropriate type, there is also a set instruction, where set.le.u32.u64 %r101, %rd12, %rd28 sets the 32-bit register %r101 to 0xffffffff if the 64-bit register %rd12 is less than or equal to the 64-bit register %rd28. Otherwise %r101 is set to ...
Kaggle is a data science competition platform and online community for data scientists and machine learning practitioners under Google LLC.Kaggle enables users to find and publish datasets, explore and build models in a web-based data science environment, work with other data scientists and machine learning engineers, and enter competitions to solve data science challenges.
CUDA code runs on both the central processing unit (CPU) and graphics processing unit (GPU). NVCC separates these two parts and sends host code (the part of code which will be run on the CPU) to a C compiler like GNU Compiler Collection (GCC) or Intel C++ Compiler (ICC) or Microsoft Visual C++ Compiler, and sends the device code (the part which will run on the GPU) to the GPU.
oneAPI is an open standard, adopted by Intel, [1] for a unified application programming interface (API) intended to be used across different computing accelerator (coprocessor) architectures, including GPUs, AI accelerators and field-programmable gate arrays. It is intended to eliminate the need for developers to maintain separate code bases ...
Hopper allows CUDA compute kernels to utilize automatic inline compression, including in individual memory allocation, which allows accessing memory at higher bandwidth. This feature does not increase the amount of memory available to the application, because the data (and thus its compressibility ) may be changed at any time.
The maximum x, y and z dimensions of a block are 1024, 1024 and 64, and it should be allocated such that x × y × z ≤ 1024, which is the maximum number of threads per block. [3] Blocks can be organized into one, two or three-dimensional grids of up to 2 31 -1, 65,535 and 65,535 blocks in the x, y and z dimensions respectively. [ 3 ]