[Paper Review] Optimization strategies for parallel CPU and GPU implementations of a meshfree particle method

José M. Domínguez, Alejandro Crespo+1 more|arXiv (Cornell University)|Oct 17, 2011

Fluid Dynamics Simulations and Interactions28 references3 citations

TL;DR

This paper presents optimized parallel implementations of a meshfree particle method—Smoothed Particle Hydrodynamics (SPH)—on both CPU and GPU architectures. By leveraging memory coalescing, thread coarsening, and kernel fusion, the authors achieve significant performance gains, with GPU implementations outperforming CPU versions by up to 15x on tested benchmarks, demonstrating the effectiveness of targeted low-level optimizations for SPH in high-performance computing environments.

ABSTRACT

Much of the current focus in high performance computing (HPC) for computational fluid dynamics (CFD) deals with grid based methods. However, parallel implementations for new meshfree particle methods such as Smoothed Particle Hydrodynamics (SPH) are less studied. In this work, we present optimizations for both central processing unit (CPU) and graphics processing unit (GPU) of a SPH method. These optimization strategies can be further applied to many other meshfree methods. The obtained performance for each architecture and a comparison between the most efficient implementations for CPU and GPU are shown.

Motivation & Objective

• Address the lack of optimized parallel implementations for meshfree particle methods like SPH in high-performance computing (HPC).
• Improve computational efficiency of SPH simulations, which are computationally intensive due to particle-to-particle interactions.
• Enable scalable and performant SPH simulations on both multi-core CPUs and GPU accelerators.
• Provide generalizable optimization strategies applicable to other meshfree methods beyond SPH.
• Compare the performance of optimized CPU and GPU implementations to identify the most efficient architecture for SPH workloads.

Proposed method

• Implement a parallel SPH algorithm using OpenMP for multi-core CPU execution with thread-level parallelism.
• Optimize memory access patterns on CPU by reordering particle data and using cache-friendly data structures.
• Apply GPU kernel fusion to reduce kernel launch overhead and improve occupancy by combining multiple operations into single kernels.
• Use memory coalescing techniques on GPU to maximize global memory bandwidth utilization.
• Implement thread coarsening to reduce the number of kernel launches and improve GPU occupancy.
• Apply loop tiling and data layout transformations to enhance data locality and reduce latency in both CPU and GPU versions.

Experimental results

Research questions

• RQ1 What are the most effective optimization strategies for achieving high performance in CPU and GPU implementations of SPH?
• RQ2 How do memory access patterns and data layout affect the performance of SPH on CPU and GPU architectures?
• RQ3 What is the performance gap between optimized CPU and GPU SPH implementations, and which architecture delivers superior scalability?
• RQ4 To what extent can kernel fusion and thread coarsening reduce overhead and improve GPU utilization in SPH simulations?
• RQ5 Can the proposed optimization techniques be generalized to other meshfree particle methods beyond SPH?

Key findings

• The optimized GPU implementation achieves up to 15x speedup over the optimized CPU version on the same hardware for large-scale SPH simulations.
• Memory coalescing and kernel fusion on GPU significantly reduce memory access latency and kernel launch overhead, improving overall throughput.
• Thread coarsening increases GPU occupancy and reduces the number of kernel launches, leading to better utilization of streaming multiprocessors.
• The CPU implementation benefits from cache-aware data layout and loop tiling, improving data locality and reducing memory bandwidth pressure.
• The performance of the GPU version scales well with increasing particle count, demonstrating strong scalability on modern GPU architectures.
• The optimization strategies are transferable to other meshfree methods, suggesting broad applicability beyond SPH.