filmov
tv
SIMD and vectorization using AVX intrinsic functions (Tutorial)
Показать описание
The best parallel programming technique you're probably not using. Using intrinsic functions to force SIMD parallelism per CPU core and gain speedups of between x4 and x16 on top of any other gains from threading etc.
Gives examples of how to use the intrinsic functions to accelerate your numerical coding.
Introductory Material (skip if you know what SIMD and intrinsics are)
00:00 Introduction
03:37 Intro to SIMD
05:17 SIMD instruction sets on x86
10:58 What are compiler intrinsics?
12:58 Simple comparison of standard C vs. AVX intrinsic summation
Basic setup of AVX for use in C/C++
15:11 Header files
16:25 Vector datatypes
18:19 Allocating memory
21:02 Intrinsic function naming 'convention'
23:55 Summary of AVX intrinsic functionality
Examples of AVX intrinsics
27:28 Intro
27:45 Arithmetic (e.g. addition, subtraction, multiplication, division) [_mm256_add_ps, _mm256_mul_ps, _mm256_div_ps]
30:53 Fused-multiply add [_mm256_fmadd_ps]
33:52 Math functions (e.g. max,min,sqrt) [_mm256_max_ps, _mm256_sqrt_ps, _mm256_rsqrt_ps]
34:33 Logical (e.g. and, or, xor) [_mm256_and_ps]
35:06 Load/store [_mm256_load_ps, _mm256_loadu_ps]
36:18 Comparisons (e.g. greater than, equals, less than) [_mm256_cmp_ps]
39:05 Branchless programming (approximating an 'if' statement in SIMD)
41:57 Permute/shuffle (rearranging elements within a vector) [_mm256_permutevar8x32_ps, _mm256_permute4x64_pd, _mm256_permute_ps]
46:20 What's a 'lane'?
49:10 Insert/extract [_mm256_insertf128_ps, _mm256_extractf128_ps]
49:51 Blend [_mm256_blend_ps]
50:30 Gather/scatter [_mm256_i32gather_ps]
52:22 Horizontal add [_mm256_hadd_ps]
53:12 Conversion (e.g. float32 to int32) [_mm256_cvtepi32_ps, _mm256_cvtps_epi32, _mm256_cvtps_pd, _mm256_cvtepi32_epi64]
53:34 Set (pseudo-intrinsic) [_mm256_set_ps, _mm256_set1_ps]
Programming example
54:45 Complex dot product
63:14 Vector reduction
Gives examples of how to use the intrinsic functions to accelerate your numerical coding.
Introductory Material (skip if you know what SIMD and intrinsics are)
00:00 Introduction
03:37 Intro to SIMD
05:17 SIMD instruction sets on x86
10:58 What are compiler intrinsics?
12:58 Simple comparison of standard C vs. AVX intrinsic summation
Basic setup of AVX for use in C/C++
15:11 Header files
16:25 Vector datatypes
18:19 Allocating memory
21:02 Intrinsic function naming 'convention'
23:55 Summary of AVX intrinsic functionality
Examples of AVX intrinsics
27:28 Intro
27:45 Arithmetic (e.g. addition, subtraction, multiplication, division) [_mm256_add_ps, _mm256_mul_ps, _mm256_div_ps]
30:53 Fused-multiply add [_mm256_fmadd_ps]
33:52 Math functions (e.g. max,min,sqrt) [_mm256_max_ps, _mm256_sqrt_ps, _mm256_rsqrt_ps]
34:33 Logical (e.g. and, or, xor) [_mm256_and_ps]
35:06 Load/store [_mm256_load_ps, _mm256_loadu_ps]
36:18 Comparisons (e.g. greater than, equals, less than) [_mm256_cmp_ps]
39:05 Branchless programming (approximating an 'if' statement in SIMD)
41:57 Permute/shuffle (rearranging elements within a vector) [_mm256_permutevar8x32_ps, _mm256_permute4x64_pd, _mm256_permute_ps]
46:20 What's a 'lane'?
49:10 Insert/extract [_mm256_insertf128_ps, _mm256_extractf128_ps]
49:51 Blend [_mm256_blend_ps]
50:30 Gather/scatter [_mm256_i32gather_ps]
52:22 Horizontal add [_mm256_hadd_ps]
53:12 Conversion (e.g. float32 to int32) [_mm256_cvtepi32_ps, _mm256_cvtps_epi32, _mm256_cvtps_pd, _mm256_cvtepi32_epi64]
53:34 Set (pseudo-intrinsic) [_mm256_set_ps, _mm256_set1_ps]
Programming example
54:45 Complex dot product
63:14 Vector reduction
1:06:15
SIMD and vectorization using AVX intrinsic functions (Tutorial)
0:10:11
AVX Explained - Performance and Syntax Analysis
0:55:39
Intrinsic Functions - Vector Processing Extensions
0:06:30
Let’s Hear it for SIMD Programming
0:06:17
Intel Instructions 59 Advanced Vector Extensions AVX
0:16:17
AVX 512 Properly Explained! – Performance and Syntax Analysis
0:09:02
What is SIMD? Abusing Vector Instructions Across Threads for Ray Tracing
0:03:21
What is SIMD ?
0:29:32
Highlight: LIVE CODING - Rust - Vector sum using SIMD AVX instructions
0:04:51
Episode 5.4 - Optimization of Vectorization: Alignment and Hints
0:02:10
SIMD AVX
0:06:03
Episode 4.1 - SIMD Parallelism and Intrinsics
1:23:23
SIMD in one Afternoon (AVX2 C++)
0:48:51
Vectorization (SIMD) and Scaling | James Reinders, Intel Corporation
0:40:30
Cray XC30 Day 2 Programming AVX Intrinsics (Intel Advanced Vector Extensions Intrinsics)
0:00:37
What is SIMD?
1:01:34
Intel Intrinsic functions for SIMD Parallelism (Vectorization).
0:01:51
Efficiently Generate a u8 Mask Using SIMD/AVX
0:17:16
Talk on VeGen, The Vectorizer Generator for SIMD and Beyond
0:14:47
Parallel C++: SIMD Intrinsics
0:09:05
SIMD and Vectorization in .NET - .NET Concept of the Week - Episode 11
0:04:22
avx explained performance and syntax analysis
0:22:30
Using SIMD to Accelerate C# Applications: the Easiest Path to a 10x Speedup.
0:20:04
Making use of simd vectorisation to improve code performance
Комментарии