Bringing Auto-tuning to HIP: Analysis of Tuning Impact and Difficulty on AMD and Nvidia GPUs

Overview of Auto-Tuning in HIP for AMD and Nvidia GPUs

The paper "Bringing Auto-tuning to HIP: Analysis of Tuning Impact and Difficulty on AMD and Nvidia GPUs" explores the performance impact and tuning difficulties associated with auto-tuning GPU kernels when deployed on both AMD and Nvidia hardware platforms. The authors achieved this by extending Kernel Tuner, an open-source auto-tuning framework, to support HIP (Heterogeneous-Compute Interface for Portability) applications, thereby enabling the capability to auto-tune GPU kernels that can run on both AMD and Nvidia GPUs.

Key Contributions

1. HIP Support in Kernel Tuner: The researchers integrated HIP support into Kernel Tuner by incorporating PyHIP, a Python library that interfaces with the HIP runtime and compiler. This extension allows Kernel Tuner to empirically measure and optimize kernel execution times on AMD and Nvidia GPUs using a unified framework.
2. Performance Impact and Tuning Difficulty: The main evaluation centered on four highly-tunable benchmark kernels: Convolution, Hotspot, Dedispersion, and GEMM. The study highlights that the performance impact of auto-tuning is substantially higher for AMD devices (10x improvement) than for Nvidia devices (2x improvement). Additionally, the tuning difficulty appears more pronounced on AMD GPUs, as denoted by the larger variance between median and optimal performance configurations.
3. Performance Portability: Another critical finding is that configurations optimized for Nvidia GPUs do not necessarily translate to high performance on AMD GPUs, demonstrating the necessity of re-tuning for AMD hardware to achieve optimal performance. However, the reverse appears more consistent; configurations tuned on AMD often perform well on Nvidia devices.

Experimental Setup and Methodology

The researchers utilized four different GPU models: two from AMD (W6600 and MI250X) and two from Nvidia (A4000 and A100). For each kernel, they analyzed performance distributions, tuning difficulties using the proportion of centrality, and performance portability across different devices using various subsets of hardware configurations.

Detailed Insights

Convolution Kernel

• Performance Impact: Auto-tuning showed significant improvements, particularly for AMD GPUs, with a 30x performance gain, compared to 3x for Nvidia.
• Tuning Difficulty: The tuning space on AMD GPUs displayed more bottom-heavy distributions indicating that optimal configurations are extreme outliers, thus making manual tuning practically infeasible.
• Configurations: Preferences for small thread blocks with strategic thread distribution in 1D or 2D were noted. The reliance on shared memory and tiling strategies varied significantly across devices.

Hotspot Kernel

• Performance Impact: Performance gains ranged from 1.9x (Nvidia A4000) to 5.3x (AMD MI250X).
• Tuning Difficulty: Server-grade GPUs appear more challenging for optimization but offer higher performance benefits from tuning.
• Configurations: Notably, the domain-specific optimizations like temporal tiling were more prevalent in configurations for AMD GPUs, underscoring the architectural nuances between GPU vendors.

Dedispersion Kernel

• Performance Impact: The MI250X achieved the highest absolute performance, showcasing the efficacy of its L2 cache utilization.
• Tuning Difficulty: The MI250X's optimal configurations were more extreme outliers when compared to other GPUs.
• Configurations: Large thread blocks and tiling optimizations highlighted the architectural dependencies for memory-bound applications.

GEMM Kernel

• Performance Impact: Variability across devices was evident with the highest performance on A100 being 1.6x of the median.
• Tuning Difficulty: Nvidia GPUs demonstrated a more gradual tuning curve when the constraints on optimality were relaxed.
• Configurations: Shared memory utilization and loop blocking emerged as key factors across all devices, although thread block sizing differed between AMD and Nvidia.

Implications and Future Work

The findings from this research have significant implications for the optimization of HIP-coded applications across heterogeneous GPU environments. The stark differences in tuning difficulty and performance improvement highlight the need for advanced auto-tuning tools like Kernel Tuner to ensure efficient and portable performance across various hardware platforms. Future research could benefit from a broader kernel variety and examination of additional GPU models to validate these findings comprehensively. Additionally, investigating the architectural underpinnings that drive these performance and tuning disparities could yield deeper insights into optimizing GPU programming models.

Conclusion

The paper provides a thorough analysis of auto-tuning effectiveness on AMD and Nvidia GPUs using HIP, underlining the necessity of re-tuning for achieving optimal performance on differing hardware architectures. The extension of Kernel Tuner to support HIP marks a valuable contribution, facilitating broader applicability and performance portability in GPU computing.