Optimize Your Programs

Learn about tips and techniques to create better performing programs, so that today’s compilers can auto-vectorize leveraging Arm SIMD extensions.


Memory Aliasing and the “Restrict” Keyword

  • Learn about the importance of using the “restrict” keyword in C correctly. When a compiler auto-vectorizes code, it first needs to be sure that this is a safe action.

Memory Latency

  • Gain a better understanding of caches, prefetching, and data alignment on Arm platforms. Learn what a programmer can do to improve this access time.

Learn about Integer and Floating-point conversions

  • This how-to guide explains how to avoid pitfalls (cases where inadvertently the developer ends up with floating point operations) and how to leverage the power of integer performance.

Leverage Auto-Vectorization in Compilers

  • Learn how to structure the flow of your program to make it easier for the compiler to perform auto-vectorization.

Modifying Loop Layout to be Auto-Vectorization Friendly

  • An efficient data layout can be the difference between a slow and very fast program. Learn how you can help the compiler, as well as how you can covert your program to hand-written SIMD code.

Optimize with Arm SIMD

Learn how to optimize in Assembly and in C/C++ using Neon, SVE, and SVE2 intrinsics. Arm intrinsics are a set of C/C++ functions whose precise implementation is known to the Arm compiler, GCC and LLVM. The LLVM (open-source Clang) version 5 and onwards includes support for SVE, and version 9 and onwards includes support for SVE2.


Arm Intrinsics Search Engine

  • The Arm intrinsics search engine can be filtered by SIMD ISA (Neon, SVE, SVE2, Helium), base type (floating point, integer, etc.), bit size, and architecture.

Optimizing C/C++ and Assembly Code with Arm SIMD

C/C++ Case Studies with Open-Source Libraries

How to Vectorize Loops with Conditional Statements

  • C compilers have limited ability to vectorize loops with conditional statements. Learn how best to use Arm Neon intrinsics to get the best optimized code from C compilers.

Neon Intrinsics for Optimized Math, Networking, and String Operations

  • How to use Neon intrinsics to accelerate a custom routine in an app, giving some practical guidance on structuring code amenable to vectorization.

Migrate from x86 and x64 to Arm Intrinsics

Learn about the different methods of porting existing x86 and x64 to Arm SIMD. And get inspired with several case studies from cloud to edge.


Porting Architecture-Specific Intrinsics

  • Learn about different libraries to migrate the x86 and x64 Intrinsics code to Arm intrinsics, and how to find intrinsics in large code bases.

Vectorscan Porting Analysis

  • Vectorscan is a portable fork of Intel’s Hyperscan. Learn about the porting challenges and the success of the porting project.

Optimize with Arm Intrinsics for Android

  • A wealth of resources on how-to get started using Arm intrinsics (Neon and SVE2) on Android’s NDK.

Porting to Arm Intrinsics with SIMDe

  • A case study on how H.266 (VVenC and VVdeC) was converted from x86 and x64 to Arm Neon with SIMDe, leveraging over 200% performance gains.

Evaluating SSE-to-Neon and SIMDe Libraries

  • Read the list of considerations to take when deciding which library would be best suited to your SIMD porting needs.

Porting Intel and AMD Intrinsics to Arm Neon Intrinsics

  • Blog going through the different porting options with the pros and cons of each, when migrating x86 or x64 code to Arm intrinsics.

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Community Support

Learn from the Community

Talk directly to an Arm expert, George Steed, and the broader Arm community involved in server and cloud computing today.

George Steed

An Arm Expert in SIMD intrinsics and performance optimisation, George has spent the last eight years working on improving the performance of maths libraries and codec implementations running on Arm.

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Think we are missing some resources? Have some examples to share from your experience? Let us know directly via the link below.