ARM Cortex-X3, A715, A510 Throughput Discrepancy in Int8 vs FP32 Multiplication

ARM Cortex-X3, A715, A510 Throughput Discrepancy in Int8 vs FP32 Multiplication

The discrepancy in throughput between Int8 and FP32 multiplication on ARM Cortex-X3, A715, and A510 processors is a nuanced issue that requires a deep understanding of the underlying microarchitectures, instruction latencies, and resource availability. The expectation of a 4x increase in throughput when switching from FP32 to Int8 multiplication is rooted in the theoretical advantage of processing four times as many Int8 elements in the same register width compared to FP32. However, the observed 2x increase on certain cores (A715 and A510) and the full 4x increase on others (X3) suggests that the actual performance is influenced by several factors, including instruction latency, resource contention, and microarchitectural differences.

The ARM Cortex-X3, A715, and A510 processors are designed with different performance and efficiency goals, which directly impact their execution pipelines, register file sizes, and throughput capabilities. The Cortex-X3 is optimized for high performance, featuring a wider execution pipeline and more resources to handle high-throughput workloads. In contrast, the Cortex-A715 and A510 are designed with a balance of performance and power efficiency, which may limit their peak throughput in certain scenarios. Understanding these differences is crucial for diagnosing the observed throughput discrepancy.

The key to resolving this issue lies in analyzing the instruction latencies, throughput rates, and resource availability for both FP32 and Int8 multiplication across these cores. The FMLA (Floating-point Multiply-Accumulate) and MLA (Integer Multiply-Accumulate) instructions have different latencies and throughput rates, which can vary depending on the core architecture. Additionally, the availability of execution units and the ability to issue multiple instructions per cycle play a significant role in determining the overall throughput.

Instruction Latency and Resource Contention in Cortex-X3, A715, and A510

The latency of both FMLA and MLA instructions is four cycles across the Cortex-X3, A715, and A510 architectures. However, the throughput rates differ due to variations in resource availability and microarchitectural design. The Cortex-X3, being a high-performance core, can sustain a higher throughput for both FP32 and Int8 operations due to its wider execution pipeline and greater resource availability. This allows it to achieve the expected 4x increase in throughput when switching from FP32 to Int8 multiplication.

On the other hand, the Cortex-A715 and A510 cores, which are designed for a balance of performance and power efficiency, have fewer resources available for parallel execution. This results in a lower throughput for Int8 multiplication compared to the Cortex-X3. Specifically, the Cortex-A715 and A510 cores can only achieve a 2x increase in throughput when switching from FP32 to Int8 multiplication. This is because the increased number of Int8 elements being processed leads to resource contention, effectively halving the throughput compared to the Cortex-X3.

The following table summarizes the throughput rates for FMLA and MLA instructions across the Cortex-X3, A715, and A510 cores:

The table highlights the differences in throughput rates and the resulting increase factors when switching from FP32 to Int8 multiplication. The Cortex-X3 achieves the expected 4x increase, while the Cortex-A715 and A510 cores only achieve a 2x increase due to resource contention.

Microarchitectural Differences and Optimization Strategies

The microarchitectural differences between the Cortex-X3, A715, and A510 cores play a significant role in determining the throughput rates for FP32 and Int8 multiplication. The Cortex-X3 features a wider execution pipeline, more execution units, and a larger register file, which allows it to handle the increased workload of Int8 multiplication without significant resource contention. This results in the expected 4x increase in throughput.

In contrast, the Cortex-A715 and A510 cores have narrower execution pipelines and fewer execution units, which limits their ability to handle the increased workload of Int8 multiplication. This leads to resource contention and a lower throughput increase factor of 2x. To optimize performance on these cores, it is essential to understand their microarchitectural limitations and adjust the workload accordingly.

One optimization strategy is to break down the workload into smaller chunks that can be processed more efficiently by the Cortex-A715 and A510 cores. This can be achieved by using techniques such as loop unrolling, vectorization, and parallelization. Additionally, fine-tuning the code to take advantage of the specific features of each core, such as the availability of SIMD (Single Instruction, Multiple Data) instructions, can help improve throughput.

Another strategy is to use performance profiling tools, such as likwid-bench and perf, to identify bottlenecks and optimize the code accordingly. These tools can provide detailed insights into the execution pipeline, resource usage, and instruction latencies, allowing for targeted optimizations that can improve throughput.

Finally, referring to the ARM Software Optimization Guide for the specific CPU type can provide valuable insights into the optimal coding practices and performance tuning techniques for each core. This guide includes detailed information on instruction latencies, throughput rates, and microarchitectural features, which can be used to fine-tune the code for maximum performance.

In conclusion, the discrepancy in throughput between Int8 and FP32 multiplication on ARM Cortex-X3, A715, and A510 processors is a complex issue that requires a deep understanding of the underlying microarchitectures and optimization strategies. By analyzing the instruction latencies, resource availability, and microarchitectural differences, it is possible to identify the root causes of the throughput discrepancy and implement targeted optimizations to achieve the desired performance.