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# Copyright (c) 2026, Modular Inc. All rights reserved.
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# Licensed under the Apache License v2.0 with LLVM Exceptions:
# https://llvm.org/LICENSE.txt
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"""Implements SIMD primitives and abstractions.

Provides high-performance SIMD primitives and abstractions for
vectorized computation in Mojo. It enables efficient data-parallel operations
by leveraging hardware vector processing units across different architectures.

Key Features:
1. Architecture-agnostic SIMD abstractions with automatic hardware detection
2. Optimized vector operations for common numerical computations
3. Explicit control over vectorization strategies and memory layouts
4. Zero-cost abstractions that compile to efficient machine code
5. Support for different vector widths and element types

Primary Components:
- Vector types: Strongly-typed vector containers with element-wise operations
- SIMD intrinsics: Low-level access to hardware SIMD instructions
- Vectorized algorithms: Common algorithms optimized for SIMD execution
- Memory utilities: Aligned memory allocation and vector load/store operations

Performance Considerations:
- Vector width selection should match target hardware capabilities
- Memory alignment affects load/store performance
- Data layout transformations may be necessary for optimal vectorization

Integration:
This module is designed to work seamlessly with other Mojo numerical computing
components, including tensor operations, linear algebra routines, and
domain-specific libraries for machine learning and scientific computing.
"""

import std.math
from std.collections import InlineArray
from std.collections.string.string import _calc_initial_buffer_size
from std.hashlib.hasher import Hasher
from std.math import Ceilable, CeilDivable, Floorable, Truncable
from std.math.math import _call_ptx_intrinsic, trunc
from std.sys import (
    CompilationTarget,
