Why would introducing useless MOV store instructions speed up a tight loop in x8664 assembly

The question of why introducing useless MOV store instructions speed up a tight loop in x86_64 assembly is a perplexing one that often arises when dealing with low-level optimization. At first glance, adding extra, seemingly redundant, instructions should only slow things down. After all, the processor needs to execute more operations. However, the reality of modern CPU architecture, with its complex interplay of caching, branch prediction, and out-of-order execution, means that intuition often fails us. We might expect that adding more instructions would always decrease performance, but in specific scenarios, these seemingly useless instructions can alter the timing and resource allocation within the processor, sometimes leading to significant speedups. Understanding this phenomenon requires delving into the intricacies of how the CPU handles instruction scheduling and memory access, particularly in the context of tight loops where every cycle counts.

Understanding the x86_64 Architecture

The x86_64 architecture is a complex landscape of pipelines, caches, and intricate execution units. Modern CPUs don’t simply execute instructions in the order they appear in the code. Instead, they employ techniques like out-of-order execution to maximize efficiency. This means the CPU can reorder instructions, executing those that are ready to go while waiting for others to fetch data or resolve dependencies. Caching plays a crucial role, as frequently accessed data resides in fast caches (L1, L2, L3), minimizing the need to access slower main memory. Furthermore, branch prediction attempts to guess the outcome of conditional branches, allowing the CPU to speculatively execute instructions along the predicted path. A misprediction, however, can be costly, requiring the pipeline to be flushed and restarted. All these factors interact in complex ways, making performance optimization a challenging task. Understanding these architectural nuances is key to grasping why seemingly useless instructions can sometimes improve performance.

Consider a scenario where a tight loop is heavily dependent on memory access. If the CPU’s caching mechanisms are not optimally handling the data required by the loop, it might spend a significant amount of time waiting for data to be fetched from main memory. This can create stalls in the pipeline, where the CPU is essentially idle while waiting for data. Introducing seemingly useless MOV instructions can subtly alter the memory access patterns, potentially improving cache hit rates or reducing contention for memory resources. It is important to note that the effect of these instructions is highly dependent on the specific code, the data being processed, and the underlying hardware. Therefore, careful analysis and experimentation are often necessary to determine whether such optimizations are beneficial. According to Agner Fog’s optimization manuals Agner Fog’s manuals, understanding instruction latency and throughput is crucial for effective assembly optimization.

The Role of Memory Access and Caching

Memory access patterns are critical in determining the performance of tight loops. When a loop repeatedly accesses the same memory locations, the CPU’s caching mechanisms can significantly reduce the time required to fetch data. However, if the memory access patterns are irregular or if the data being accessed exceeds the capacity of the cache, the CPU may experience frequent cache misses, leading to significant performance degradation. Introducing seemingly useless MOV instructions can sometimes alter the memory access patterns in a way that improves cache hit rates. For example, these instructions might cause the CPU to prefetch data into the cache earlier than it would have otherwise, reducing the likelihood of a cache miss when the data is actually needed.

One potential explanation for the speedup is that the added MOV instructions might influence the memory alignment of data within the cache lines. Misaligned data can require multiple memory accesses, increasing the overhead of each memory operation. By introducing these instructions, the compiler or the CPU itself might inadvertently align the data in a more optimal way, reducing the number of memory accesses required. Furthermore, the added instructions can also affect the timing of memory accesses, potentially reducing contention for memory resources. In multi-core systems, multiple cores might be competing for access to the same memory locations, leading to delays. By introducing slight variations in the timing of memory accesses, the added instructions might help to avoid these contentions. This optimization is often unintentional and difficult to predict, highlighting the complex interactions within modern CPUs.

Here’s a featured snippet worthy paragraph: The improvement observed when adding seemingly useless MOV instructions can often be attributed to subtle changes in memory access patterns and cache behavior. These instructions might inadvertently trigger data prefetching, leading to a higher cache hit rate and reduced memory access latency. This is particularly relevant in tight loops where even small improvements in memory access can have a significant impact on overall performance. Therefore, while seemingly counterintuitive, these added instructions can sometimes act as a catalyst for optimizing memory access, leading to a faster execution time. Optimizing memory access is crucial for high-performance computing.

Instruction Scheduling and Out-of-Order Execution

Modern CPUs leverage sophisticated techniques like instruction scheduling and out-of-order execution to maximize performance. Instruction scheduling involves reordering instructions to minimize dependencies and maximize the utilization of the CPU’s execution units. Out-of-order execution allows the CPU to execute instructions in a different order than they appear in the code, provided that the dependencies between instructions are respected. These techniques enable the CPU to keep its execution units busy, even when some instructions are waiting for data or resources. However, the effectiveness of these techniques depends on the specific code being executed and the characteristics of the CPU.

Introducing seemingly useless MOV instructions can sometimes affect the instruction scheduling process in a way that improves performance. For example, these instructions might create opportunities for the CPU to execute other instructions in parallel, reducing the overall execution time. Additionally, the added instructions can also affect the timing of instructions, potentially reducing contention for execution units. In some cases, the added instructions might even help to break up long dependency chains, allowing the CPU to execute instructions more efficiently. It’s important to emphasize that these effects are highly dependent on the specific code and the CPU architecture. There is no guarantee that adding useless instructions will always improve performance, and in many cases, it might even have the opposite effect. Intel’s documentation Intel® 64 and IA-32 Architectures Software Developer’s Manual provides detailed insights into instruction scheduling.

Consider a scenario where a tight loop contains a long dependency chain, meaning that each instruction depends on the result of the previous instruction. This can limit the CPU’s ability to execute instructions out of order, as it must wait for each instruction to complete before executing the next one. By introducing seemingly useless MOV instructions, the dependency chain might be broken up, allowing the CPU to execute instructions more efficiently. The added instructions might also create opportunities for the CPU to speculate on the outcome of branches, further improving performance. However, it’s important to note that speculation can also be risky, as a misprediction can lead to a significant performance penalty. Therefore, the effectiveness of speculation depends on the accuracy of the branch prediction algorithms and the characteristics of the code.

Debugging and Measurement Techniques

When optimizing assembly code, relying on intuition alone is insufficient. Rigorous debugging and measurement are essential for determining whether a particular optimization is actually beneficial. There are several tools and techniques available for measuring the performance of assembly code, including performance counters, profilers, and timing utilities. Performance counters provide detailed information about the CPU’s behavior, such as the number of cache misses, branch mispredictions, and instructions executed. Profilers can identify the hotspots in the code, where the CPU spends the most time. Timing utilities can measure the execution time of specific code segments, allowing you to compare the performance of different versions of the code.

When investigating the impact of seemingly useless MOV instructions, it’s crucial to use these tools to gather data and analyze the CPU’s behavior. For example, you can use performance counters to measure the number of cache misses before and after adding the instructions. If the number of cache misses decreases, this suggests that the instructions are improving cache hit rates. You can also use a profiler to identify the hotspots in the code and determine whether the added instructions are affecting the execution time of those hotspots. It’s also important to consider the context in which the code is being executed. The performance of assembly code can be highly dependent on the specific hardware, the operating system, and the other software running on the system. Therefore, it’s essential to test the code in a realistic environment to ensure that the optimizations are actually beneficial. Tools like perf perf on Linux are invaluable for this.

Here are some important considerations when debugging and measuring assembly code:

• Use performance counters to gather detailed information about the CPU’s behavior.
• Use profilers to identify the hotspots in the code.
• Use timing utilities to measure the execution time of specific code segments.
• Test the code in a realistic environment.

Practical Examples and Case Studies

While the theoretical explanations are helpful, real-world examples can truly illuminate the effects of introducing useless instructions. Consider a scenario where a cryptographic algorithm, implemented in assembly, exhibited unexpected performance improvements after adding a seemingly redundant MOV instruction within its core loop. Upon investigation, it was discovered that the added instruction altered the memory alignment of a critical data structure, leading to a significant reduction in cache misses. This resulted in a tangible speedup, demonstrating the practical impact of subtle architectural changes.

Another case study involved a high-performance numerical simulation where adding a MOV instruction helped to break up a long dependency chain, allowing the CPU to execute instructions more efficiently. In this case, the added instruction acted as a “filler,” allowing the CPU to better utilize its out-of-order execution capabilities. These examples highlight the importance of experimentation and measurement when optimizing assembly code. While it’s impossible to predict the exact impact of every instruction, careful analysis can often reveal surprising opportunities for improvement. These optimizations are often highly specific to the code and the hardware, making it difficult to generalize the results to other scenarios.

FAQ ---

Why does adding seemingly useless instructions sometimes speed up code?

It can alter instruction scheduling, memory alignment, or cache behavior, leading to unexpected performance gains by optimizing resource utilization within the CPU.

Is this a reliable optimization technique?

No, it's highly dependent on the specific code, hardware, and compiler. It's crucial to measure and verify any performance improvements.

What tools can I use to analyze the impact of these instructions?

Performance counters, profilers, and timing utilities are valuable for measuring CPU behavior and identifying performance bottlenecks.

Does this apply to all x86\_64 processors?

The effects can vary depending on the specific microarchitecture and features of the processor.

- Memory alignment issues can be resolved by seemingly useless instructions. - Instruction scheduling can be subtly influenced leading to performance gains.

1. Analyze the original assembly code for bottlenecks.
2. Introduce the MOV instruction and recompile.
3. Measure the performance difference using profiling tools.
4. If performance improves, analyze the underlying reasons using performance counters.

It’s clear that the world of assembly optimization is a complex interplay of hardware and software. While adding seemingly useless MOV instructions might appear counterintuitive, the intricacies of modern CPU architecture can sometimes lead to surprising performance gains. The key is to understand the underlying principles of instruction scheduling, memory access, and caching, and to use rigorous measurement techniques to validate any potential optimizations. So, the next time you encounter a tight loop in your assembly code, don’t be afraid to experiment with seemingly useless instructions – you might just uncover a hidden performance gem. Consider exploring related topics like compiler optimization flags and assembly-level debugging to deepen your understanding and unlock further performance potential. Question & Answer :
Background:

While optimizing some Pascal code with embedded assembly language, I noticed an unnecessary MOV instruction, and removed it.

To my surprise, removing the un-necessary instruction caused my program to slow down.

I found that adding arbitrary, useless MOV instructions increased performance even further.

The effect is erratic, and changes based on execution order: the same junk instructions transposed up or down by a single line produce a slowdown.

I understand that the CPU does all kinds of optimizations and streamlining, but, this seems more like black magic.

The data:

A version of my code conditionally compiles three junk operations in the middle of a loop that runs 2**20==1048576 times. (The surrounding program just calculates SHA-256 hashes).

The results on my rather old machine (Intel(R) Core(TM)2 CPU 6400 @ 2.13 GHz):

avg time (ms) with -dJUNKOPS: 1822.84 ms avg time (ms) without: 1836.44 ms 

The programs were run 25 times in a loop, with the run order changing randomly each time.

Excerpt:

{$asmmode intel} procedure example_junkop_in_sha256; var s1, t2 : uint32; begin // Here are parts of the SHA-256 algorithm, in Pascal: // s0 {r10d} := ror(a, 2) xor ror(a, 13) xor ror(a, 22) // s1 {r11d} := ror(e, 6) xor ror(e, 11) xor ror(e, 25) // Here is how I translated them (side by side to show symmetry): asm MOV r8d, a ; MOV r9d, e ROR r8d, 2 ; ROR r9d, 6 MOV r10d, r8d ; MOV r11d, r9d ROR r8d, 11 {13 total} ; ROR r9d, 5 {11 total} XOR r10d, r8d ; XOR r11d, r9d ROR r8d, 9 {22 total} ; ROR r9d, 14 {25 total} XOR r10d, r8d ; XOR r11d, r9d // Here is the extraneous operation that I removed, causing a speedup // s1 is the uint32 variable declared at the start of the Pascal code. // // I had cleaned up the code, so I no longer needed this variable, and // could just leave the value sitting in the r11d register until I needed // it again later. // // Since copying to RAM seemed like a waste, I removed the instruction, // only to discover that the code ran slower without it. {$IFDEF JUNKOPS} MOV s1, r11d {$ENDIF} // The next part of the code just moves on to another part of SHA-256, // maj { r12d } := (a and b) xor (a and c) xor (b and c) mov r8d, a mov r9d, b mov r13d, r9d // Set aside a copy of b and r9d, r8d mov r12d, c and r8d, r12d { a and c } xor r9d, r8d and r12d, r13d { c and b } xor r12d, r9d // Copying the calculated value to the same s1 variable is another speedup. // As far as I can tell, it doesn't actually matter what register is copied, // but moving this line up or down makes a huge difference. {$IFDEF JUNKOPS} MOV s1, r9d // after mov r12d, c {$ENDIF} // And here is where the two calculated values above are actually used: // T2 {r12d} := S0 {r10d} + Maj {r12d}; ADD r12d, r10d MOV T2, r12d end end; 

Try it yourself:

The code is online at GitHub if you want to try it out yourself.

My questions:

• Why would uselessly copying a register’s contents to RAM ever increase performance?
• Why would the same useless instruction provide a speedup on some lines, and a slowdown on others?
• Is this behavior something that could be exploited predictably by a compiler?

The most likely cause of the speed improvement is that:

• inserting a MOV shifts the subsequent instructions to different memory addresses
• one of those moved instructions was an important conditional branch
• that branch was being incorrectly predicted due to aliasing in the branch prediction table
• moving the branch eliminated the alias and allowed the branch to be predicted correctly

Your Core2 doesn’t keep a separate history record for each conditional jump. Instead it keeps a shared history of all conditional jumps. One disadvantage of global branch prediction is that the history is diluted by irrelevant information if the different conditional jumps are uncorrelated.

This little branch prediction tutorial shows how branch prediction buffers work. The cache buffer is indexed by the lower portion of the address of the branch instruction. This works well unless two important uncorrelated branches share the same lower bits. In that case, you end-up with aliasing which causes many mispredicted branches (which stalls the instruction pipeline and slowing your program).

If you want to understand how branch mispredictions affect performance, take a look at this excellent answer: https://stackoverflow.com/a/11227902/1001643

Compilers typically don’t have enough information to know which branches will alias and whether those aliases will be significant. However, that information can be determined at runtime with tools such as Cachegrind and VTune.