Improving GPU Performance by Reducing Instruction Cache Misses

Summary

GPU performance can be significantly impacted by instruction cache misses, particularly in workloads with large instruction footprints. This article explores how reducing instruction cache misses can improve GPU performance, focusing on a genomics workload using the Smith-Waterman algorithm. By adjusting loop unrolling strategies and minimizing the instruction memory footprint, developers can achieve better performance and warp occupancy.

Understanding GPU Performance Bottlenecks

GPUs are designed to process vast amounts of data quickly, equipped with compute resources known as streaming multiprocessors (SMs) and various facilities to ensure a steady data flow. However, data starvation can still occur, leading to performance bottlenecks. In some cases, SMs are starved not for data but for instructions, causing significant performance degradation.

The Impact of Instruction Cache Misses

Instruction cache misses occur when the SMs cannot fetch instructions quickly enough from memory. This can happen when the workload size increases, leading to a diverse set of instructions that the cache struggles to accommodate. In a genomics workload scenario, the investigation revealed that instruction cache misses were the primary cause of performance degradation.

Identifying the Problem

To identify the bottleneck, the NVIDIA Nsight Compute tool was used to analyze the workload. The report showed that the SMs occasionally faced data starvation, not due to a lack of data but due to instruction cache misses. The workload, composed of numerous small problems, caused uneven distribution across the SMs, leading to idle periods for some while others continued processing.

Addressing the Tail Effect

The investigation suggested increasing the workload size to mitigate the tail effect. However, this approach led to unexpected performance deterioration. The NVIDIA Nsight Compute report indicated that the primary issue was the rapid increase in warp stalls due to instruction cache misses.

Solving the Problem

The key to resolving this issue lies in reducing the overall instruction footprint, particularly by adjusting loop unrolling in the code. Loop unrolling, while beneficial for performance optimization, increases the number of instructions and register usage, potentially exacerbating cache pressure.

Experimenting with Loop Unrolling

The investigation experimented with varying levels of loop unrolling for the two outermost loops in the kernel. The findings suggested that minimal unrolling, specifically unrolling the second-level loop by a factor of 2 while avoiding unrolling the top-level loop, yielded the best performance. This approach reduced instruction cache misses and improved warp occupancy, balancing performance across different workload sizes.

Analyzing the Results

Further analysis of the NVIDIA Nsight Compute reports confirmed that reducing the instruction memory footprint in the hottest parts of the code significantly alleviated instruction cache pressure. This optimized approach led to better overall GPU performance, particularly for larger workloads.

Key Takeaways

• Instruction cache misses can cause performance degradation: In workloads with large instruction footprints, instruction cache misses can lead to significant performance degradation.
• Loop unrolling can exacerbate cache pressure: While beneficial for performance optimization, loop unrolling can increase the number of instructions and register usage, potentially exacerbating cache pressure.
• Minimal loop unrolling can improve performance: Minimal unrolling, specifically unrolling the second-level loop by a factor of 2 while avoiding unrolling the top-level loop, can yield the best performance.
• Reducing instruction memory footprint is crucial: Reducing the instruction memory footprint in the hottest parts of the code can significantly alleviate instruction cache pressure.

Table: Impact of Loop Unrolling on Instruction Cache Misses

Table: Performance Comparison Across Different Workload Sizes

Conclusion

In conclusion, reducing instruction cache misses is crucial for improving GPU performance, particularly in workloads with large instruction footprints. By adjusting loop unrolling strategies and minimizing the instruction memory footprint, developers can achieve better performance and warp occupancy. Experimenting with different compiler hints and loop unrolling strategies can help developers find the optimal approach for their specific workload.

Conclusion

Instruction cache misses can severely impact GPU performance, especially in workloads with large instruction footprints. By experimenting with different compiler hints and loop unrolling strategies, developers can achieve optimal code performance with reduced instruction cache pressure and improved warp occupancy.