There are several potential reasons why Cortex-M4 assembly code may execute slower than expected. The most common causes include inefficient code, pipeline stalls, memory access delays, and incorrect cycle timing assumptions.
One of the most frequent reasons assembly runs slower is simply inefficient code. Some common examples include:
- Unnecessary instructions – Extra instructions that don’t contribute to the end result will waste cycles.
- Suboptimal instruction ordering – Careful reordering can improve pipelining and avoid stalls.
- Excessive branching – Branches disrupt pipelining, so minimizing branches improves performance.
- Redundant memory access – Accessing memory takes much longer than register operations, so minimize loads/stores.
- Slow instructions – Some instructions likedivisions and multiplies are slower than adds/subtracts.
Reviewing algorithm efficiency and minimizing instruction count generally yields faster code. Tools like profilers can identify hot spots for optimization.
Pipelining in modern CPUs allows multiple instructions to be in-flight simultaneously. The Cortex-M4 has a 3-stage pipeline. Pipeline stalls occur when one stage must wait for another to complete before proceeding. Common causes of stalls include:
- Data dependencies – When an instruction depends on results from a previous instruction.
- Branch mispredictions – The CPU guesses branch outcomes and pipeline must flush on a mispredict.
- Memory access conflicts – Waiting for shared memory resources to become available.
- Structural hazards – Resource conflicts for execution units or registers.
Careful scheduling to avoid data hazards and efficient branching can help minimize pipeline stalls. Tools like stall cycle counters help identify frequent stall points.
Memory Access Delays
Memory access is much slower than register operations. It takes multiple cycles to load data from memory into registers or store register contents to memory. Delays include:
- Address generation – Calculating memory access addresses.
- Queueing – Waiting for access to shared buses/memory resources.
- Bus transfer – Actual data transfers over the bus.
- Cache misses – Waiting for data not in faster cache memory.
Techniques like loop tiling, data locality, and manual prefetching can help hide memory latency. But ultimately, minimizing loads/stores is key to peak performance.
Incorrect Cycle Timing Estimates
Estimating cycle timing is complex. Some key things that can lead to inaccurate estimates:
- Assuming ideal CPI (cycles per instruction) – Real world CPI is often much higher due to stalls.
- Not accounting for branch penalties – Branches disrupt pipelining for multiple cycles.
- Underestimating memory access time – Cache misses and bus contention add cycles.
- Ignoring instruction ordering – Hazards may force sequencing that adds cycles.
- Overlooking slow instructions – Some instructions have 10+ cycle latency.
Static timing analysis tools can automatically account for many of these factors. But real-world testing is still necessary to confirm cycle estimates, especially for pipelines and memory.
Tips for Improving Performance
Some tips for investigating and improving Cortex-M4 performance include:
- Use profiler tools to find hot spots and optimization opportunities.
- Minimize unnecessary branches and optimize branch prediction.
- Reorder code to avoid pipeline stalls from data hazards.
- Reduce unnecessary memory accesses and optimize caching.
- Verify cycle timing estimates, especially for branches and memory.
- Replace slow operations like multiply/divide with faster alternatives.
- Unroll small loops to reduce branch penalties.
- Use SIMD instructions to execute multiple operations simultaneously.
With complex pipelined architectures like Cortex-M4, real-world testing is key. Profile on actual hardware and confirm cycle counts for critical code segments. Optimization is often an iterative process.
Common Optimization Techniques
Here is an overview of some common optimization techniques for improving Cortex-M4 performance:
Loop unrolling reduces branch instructions by unrolling small loop bodies. This avoids branch penalties and facilitates better pipelining. But it also increases code size, so only unroll small loops.
Loop pipelining staggers execution of consecutive loop iterations to avoid data hazards. This enables multiple iterations to be in-flight simultaneously while minimizing stalls.
SIMD (single instruction, multiple data) exploits data level parallelism to execute multiple operations per instruction. This significantly improves arithmetic and memory throughput.
Prefetching brings data into cache before it is needed. This hides memory latency by overlapping prefetch with computation. But inserting prefetch too early or late reduces benefits.
Optimizing for data locality keeps data in registers and cache as long as possible. This minimizes expensive memory access. Techniques like loop tiling help improve locality.
Lazy evaluation defers work until necessary, avoiding computations that are unused. This reduces unnecessary instructions wasted on speculative results.
There are many other techniques like branch prediction, trace scheduling, etc. Profilers and static timing analysis help guide optimization efforts for maximal gains.
Hardware configuration can also impact Cortex-M4 performance. Key factors include:
- Clock speed – Faster clock means more cycles per second executed.
- Bus width – Wider buses transfer more data per cycle.
- Memory speed – Faster memories reduce access latency.
- Cache size – Larger caches reduce expensive misses.
- Core revisions – Newer cores have microarchitecture tweaks.
- Manufacturing process – Smaller processes have higher clocks.
Upgrading hardware like using faster external memory, wider buses, and newer chip revisions can provide “free” performance gains. But ultimately, efficient software optimization is still critical.
Tools for Analysis and Optimization
Useful tools for improving Cortex-M4 performance include:
- Compilers – Leverage optimizations in compiler settings.
- Profilers – Identify hot spots and optimization opportunities.
- Static timing analyzers – Help estimate cycle timing.
- Disassemblers – View generated machine code for analysis.
- Debuggers – Instrument code and collect timing data.
- Simulators – Model pipeline behavior and cache interactions.
- Assemblers – Hand tune assembly code optimizations.
Best results come from a combination of approaches. Profilers find hot spots, then static timing and simulation estimate gains, and finally debuggers confirm improvements on hardware.
When to Optimize
Premature optimization before profiling often wastes effort. Follow these guidelines on when to optimize:
- First make it correct, then make it clear, then make it fast.
- Only optimize once code is functionally complete and profiled.
- Focus optimization efforts only on frequently executed hot spots.
- Optimizing cold code gives little benefit overall.
- Sometimes easier optimization opportunities exist elsewhere.
- Amdahl’s Law defines theoretical speedup limits.
Balance optimizations with readability and maintainability. Optimize iteratively on real hardware measurements. Optimization is often unnecessary until requirements demand it.
When Assembly Beats Compiler Output
Modern optimizing compilers are very good, but sometimes hand-written assembly still wins. Some cases where assembly can outperform compiler output:
- Implementing new complex Instructions not recognized by compiler.
- Unrolling small loops for pipelining.
- Optimizing register allocation and instruction ordering.
- Minimizing branches for tighter pipelining.
- Application-specific optimizations too complex for compiler analysis.
- Optimization restricted by ISA compatibilities.
- Workarounds for hardware quirks/errata unknown to compiler.
But assembly programming is more complex and time consuming. Only use assembly where measurement proves benefits outweigh costs.
Optimizing Cortex-M4 performance requires a multipronged approach. Efficient algorithms minimize instructions. Careful pipelining and prefetching hide latency. Tools guide analysis and optimization efforts. Measurements on real hardware confirm gains. Finding the right balance builds faster and more efficient software.