daxpy-simt-soa

Using a Struct of arrays and single instruction multiple thread strategy to perform double precision a*x+y large amounts of data efficiently in c++

Why use SIMT + SoA:

Using a struct of arrays (SoA) can be advantageous in multithreaded scenarios, especially with a large number of threads, for several reasons:

• Memory Access Patterns: SoA improves cache coherence. When threads operate on separate arrays, they can access contiguous memory more efficiently, reducing cache misses and improving overall performance.

• Data Locality: In scenarios where threads operate on specific attributes of data structures, SoA allows threads to focus on a specific array without needing to pull entire structures into memory. This can minimize unnecessary data loading and improve data locality.

• Parallel Processing: SoA can simplify parallelization, especially when using SIMD (Single Instruction, Multiple Data) operations, as it allows for more efficient vectorization.

• Reduced False Sharing: When multiple threads operate on different elements of a struct, they may inadvertently cause cache line contention. With SoA, you can organize data in such a way that minimizes false sharing, as threads are less likely to modify adjacent data in memory.

• Flexible Scalability: SoA can be more scalable in terms of data processing since each thread can independently process different arrays, making it easier to distribute work.

When to Use:

• High Data Throughput Needs: If your application processes a lot of data in parallel (e.g., simulations, graphics processing).
• Specific Use Cases: When you're dealing with high-dimensional data or specific operations that can benefit from SIMD, like vector math.
• Data Separation: If your operations on the data primarily involve accessing or modifying specific fields independently, SoA is a good fit.

When Not to Use:

• Complex Data Relationships: If data is tightly coupled or requires frequent access to complete records, a traditional array of structs (AoS) might be more suitable.
• Simplicity Over Performance: In simpler applications where performance isn't a critical concern, sticking with AoS may reduce complexity and improve readability.

In summary, consider a struct of arrays when optimizing for performance in a highly parallelized environment, particularly if memory access patterns and data locality are critical to your application's efficiency.

What is DAXPY:

DAXPY stands for "Double-Precision A·X Plus Y." It is a well-known operation in linear algebra, particularly used in the context of vector and matrix computations. The DAXPY operation computes the result of multiplying a scalar ( a ) by a vector ( X ) and then adding that result to another vector ( Y ). Mathematically, it can be expressed as:

Y := a * X + Y

Here:

• 'a' is a scalar,
• 'X' and 'Y' are vectors of the same size,
• The operation updates Y with the new computed values.

DAXPY is commonly used in high-performance computing and is part of the BLAS (Basic Linear Algebra Subprograms) library, which provides efficient implementations of various linear algebra operations. It’s particularly efficient for vector operations due to its simplicity and the ability to exploit parallelism in modern hardware.

Usage

g++ -std=c++20 -o simt main.cpp
chmod +x simt
./simt