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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.
NumPy (pronounced / ˈ n ʌ m p aɪ / NUM-py) is a library for the Python programming language, adding support for large, multi-dimensional arrays and matrices, along with a large collection of high-level mathematical functions to operate on these arrays. [3]
Python has array index and array slicing expressions in lists, denoted as a[key], a [start: stop] or a [start: stop: step]. Indexes are zero-based, and negative indexes are relative to the end. Slices take elements from the start index up to, but not including, the stop index.
It is a direct search method (based on function comparison) and is often applied to nonlinear optimization problems for which derivatives may not be known. However, the Nelder–Mead technique is a heuristic search method that can converge to non-stationary points [ 1 ] on problems that can be solved by alternative methods.
Fast Half Float Conversions; Analog Devices variant (four-bit exponent) C source code to convert between IEEE double, single, and half precision can be found here; Java source code for half-precision floating-point conversion; Half precision floating point for one of the extended GCC features
In machine learning, the term tensor informally refers to two different concepts (i) a way of organizing data and (ii) a multilinear (tensor) transformation. Data may be organized in a multidimensional array (M-way array), informally referred to as a "data tensor"; however, in the strict mathematical sense, a tensor is a multilinear mapping over a set of domain vector spaces to a range vector ...
The binary format is: 1 sign bit; 8 exponent bits; 10 fraction bits (also called mantissa, or precision bits) The total 19 bits fits within a double word (32 bits), and while it lacks precision compared with a normal 32 bit IEEE 754 floating point number, provides much faster computation, up to 8 times on a A100 (compared to a V100 using FP32).
The bfloat16 format, being a shortened IEEE 754 single-precision 32-bit float, allows for fast conversion to and from an IEEE 754 single-precision 32-bit float; in conversion to the bfloat16 format, the exponent bits are preserved while the significand field can be reduced by truncation (thus corresponding to round toward 0) or other rounding ...