simd Algorithm
The Single Instruction Multiple Data (SIMD) algorithm is a parallel computing technique that involves executing the same operation on multiple data elements simultaneously. This approach is particularly useful in cases where the data elements share a common structure, and the same operation must be applied to each of them. SIMD is a type of data-level parallelism and is widely used in various scientific and engineering applications, such as image and signal processing, computer graphics, and machine learning, where the need for processing large amounts of data in parallel is prevalent. SIMD is supported by many modern processors through specialized instruction set extensions, such as Intel's Advanced Vector Extensions (AVX) and ARM's NEON.
One of the main advantages of SIMD algorithms is their ability to significantly speed up computations by exploiting the inherent parallelism in the data. By performing the same operation on multiple data elements concurrently, SIMD algorithms can achieve higher throughput and better resource utilization compared to scalar algorithms that process data elements sequentially. SIMD algorithms can also help in reducing energy consumption, as executing one instruction on multiple data elements typically consumes less energy than executing multiple instructions for the same task. However, SIMD algorithms may not be suitable for all types of computations, as they require the data to be organized in a specific format and may introduce overhead in cases where the data elements do not exhibit sufficient parallelism.
// Licensed under the Apache License, Version 2.0 <LICENSE-APACHE or
// http://www.apache.org/licenses/LICENSE-2.0> or the MIT license
// <LICENSE-MIT or http://opensource.org/licenses/MIT>, at your
// option. This file may not be copied, modified, or distributed
// except according to those terms.
pub use self::fake::*;
pub trait SimdExt {
fn simd_eq(self, rhs: Self) -> Self;
}
impl SimdExt for fake::u32x4 {
fn simd_eq(self, rhs: Self) -> Self {
if self == rhs {
fake::u32x4(0xffffffff, 0xffffffff, 0xffffffff, 0xffffffff)
} else {
fake::u32x4(0, 0, 0, 0)
}
}
}
mod fake {
use std::ops::{Add, BitAnd, BitOr, BitXor, Shl, Shr, Sub};
#[derive(Clone, Copy, PartialEq, Eq)]
#[allow(non_camel_case_types)]
pub struct u32x4(pub u32, pub u32, pub u32, pub u32);
impl Add for u32x4 {
type Output = u32x4;
fn add(self, rhs: u32x4) -> u32x4 {
u32x4(
self.0.wrapping_add(rhs.0),
self.1.wrapping_add(rhs.1),
self.2.wrapping_add(rhs.2),
self.3.wrapping_add(rhs.3))
}
}
impl Sub for u32x4 {
type Output = u32x4;
fn sub(self, rhs: u32x4) -> u32x4 {
u32x4(
self.0.wrapping_sub(rhs.0),
self.1.wrapping_sub(rhs.1),
self.2.wrapping_sub(rhs.2),
self.3.wrapping_sub(rhs.3))
}
}
impl BitAnd for u32x4 {
type Output = u32x4;
fn bitand(self, rhs: u32x4) -> u32x4 {
u32x4(self.0 & rhs.0, self.1 & rhs.1, self.2 & rhs.2, self.3 & rhs.3)
}
}
impl BitOr for u32x4 {
type Output = u32x4;
fn bitor(self, rhs: u32x4) -> u32x4 {
u32x4(self.0 | rhs.0, self.1 | rhs.1, self.2 | rhs.2, self.3 | rhs.3)
}
}
impl BitXor for u32x4 {
type Output = u32x4;
fn bitxor(self, rhs: u32x4) -> u32x4 {
u32x4(self.0 ^ rhs.0, self.1 ^ rhs.1, self.2 ^ rhs.2, self.3 ^ rhs.3)
}
}
impl Shl<usize> for u32x4 {
type Output = u32x4;
fn shl(self, amt: usize) -> u32x4 {
u32x4(self.0 << amt, self.1 << amt, self.2 << amt, self.3 << amt)
}
}
impl Shl<u32x4> for u32x4 {
type Output = u32x4;
fn shl(self, rhs: u32x4) -> u32x4 {
u32x4(self.0 << rhs.0, self.1 << rhs.1, self.2 << rhs.2, self.3 << rhs.3)
}
}
impl Shr<usize> for u32x4 {
type Output = u32x4;
fn shr(self, amt: usize) -> u32x4 {
u32x4(self.0 >> amt, self.1 >> amt, self.2 >> amt, self.3 >> amt)
}
}
impl Shr<u32x4> for u32x4 {
type Output = u32x4;
fn shr(self, rhs: u32x4) -> u32x4 {
u32x4(self.0 >> rhs.0, self.1 >> rhs.1, self.2 >> rhs.2, self.3 >> rhs.3)
}
}
#[derive(Clone, Copy)]
#[allow(non_camel_case_types)]
pub struct u64x2(pub u64, pub u64);
impl Add for u64x2 {
type Output = u64x2;
fn add(self, rhs: u64x2) -> u64x2 {
u64x2(self.0.wrapping_add(rhs.0), self.1.wrapping_add(rhs.1))
}
}
}
