Project 4: The uOp Cache Prober

Build a microbenchmark that finds the uOp cache boundary and reveals front-end behavior.

Quick Reference

Attribute Value
Difficulty Level 3: Advanced
Time Estimate 1-2 weeks
Main Programming Language Assembly (x86-64) (Alternatives: C with inline asm)
Alternative Programming Languages C (with __asm__)
Coolness Level Level 4: Hardcore Tech Flex
Business Potential 1. The “Resume Gold”
Prerequisites Assembly basics, instruction cache idea, timing with RDTSC
Key Topics uOp cache, decode pipeline, loop alignment, instruction fetch bandwidth

1. Learning Objectives

By completing this project, you will:

  1. Explain what the uOp cache is and why it exists.
  2. Design loops that isolate uOp cache behavior from decode behavior.
  3. Measure throughput changes as loop size crosses cache boundaries.
  4. Control alignment to reduce confounding effects.
  5. Produce a report that estimates uOp cache capacity in uOps.

2. All Theory Needed (Per-Concept Breakdown)

2.1 uOp Cache and Front-End Decode Pipeline

Fundamentals

The uOp cache stores decoded micro-operations (uOps) so the CPU can bypass expensive instruction decoding on hot loops. The front-end normally fetches instruction bytes, decodes them into uOps, and feeds the backend. When a loop fits in the uOp cache, the CPU can skip decoding and deliver uOps faster and with lower power. When it does not fit, the front-end must decode each time, which reduces throughput. Understanding uOp cache behavior is essential for predicting how tight loops perform.

Additional fundamentals for uOp Cache and Front-End Decode Pipeline: focus on the simplest mental model and the most common unit of measurement. Identify what changes state, what observes that state, and which constraints are non-negotiable. This keeps the concept grounded before moving to deeper microarchitectural details.

Deep Dive into the concept

Modern x86 CPUs translate complex instructions into simpler uOps. The decode pipeline typically includes instruction fetch, pre-decode, and multiple decoders (simple and complex). The decode bandwidth is limited, often 4 to 6 uOps per cycle, and some instructions require the complex decoder, which is slower. To avoid repeatedly decoding the same hot loop, the CPU can store the uOps in a uOp cache (also called the Decoded I-Cache). When the loop is executed again and the uOp cache hit occurs, the front-end can deliver a higher uOp throughput and with less variability.

The uOp cache is indexed by instruction address and is typically organized in sets and ways, like a cache. It has a capacity measured in uOps, not bytes. A loop with many simple instructions might occupy fewer uOps, whereas complex instructions (e.g., division or string ops) may expand into many uOps and consume cache capacity faster. The uOp cache also has alignment and line-size effects: a loop that crosses certain boundaries can require multiple uOp cache lines, reducing effective capacity. This is why aligning loops to cache line boundaries can improve performance.

When the uOp cache hits, the decode stage is bypassed, and the front-end feeds uOps directly into the scheduler. This can increase sustained throughput for a loop. When it misses, the front-end must decode instructions every iteration, and the throughput falls to the decode limit. Your prober exploits this by creating loops of increasing size (in number of uOps) and measuring cycles per iteration. You should see a step change when the loop no longer fits. The exact boundary depends on CPU model and microcode, but the shape is robust: a flat low-latency region followed by a higher-latency region.

A key complication is that the instruction cache (L1I) can also limit front-end throughput. If the loop is large enough to spill out of L1I, you will see a different kind of performance degradation. Therefore, your prober should focus on loop sizes that stay within L1I but exceed uOp cache capacity. You can also include a control test that uses NOPs to maintain byte size but control uOp count, helping separate decode vs uOp cache effects.

Another complication is that the uOp cache may be bypassed for certain instruction mixes or when microcode assists are involved. It may not cache certain complex instructions. Therefore, your loop should use simple, cacheable instructions (e.g., add, xor, nop) to ensure the uOp cache is exercised. Use unrolled loops to control uOp count precisely and align the loop entry to fixed boundaries using .align directives.

Additional deep dive considerations for uOp Cache and Front-End Decode Pipeline: In real designs, uOp Cache and Front-End Decode Pipeline is rarely isolated; it interacts with pipeline depth, power management, compiler decisions, and even microcode updates. When you study this behavior, vary one knob at a time and hold everything else constant: pin the core, fix the frequency if possible, warm up caches and predictors, and record the exact compiler flags. Vendor manuals describe typical behavior, but the actual thresholds can shift across steppings or microcode revisions, so empirical measurement is the ground truth. If your results disagree with published numbers, investigate confounders such as alignment, instruction form, address mapping, or hidden dependencies introduced by the compiler. From a software perspective, compilers and JITs implicitly target uOp Cache and Front-End Decode Pipeline via instruction selection, scheduling, and unrolling, so your measurements should be translated into actionable rules of thumb. Finally, validate with at least two workloads: a synthetic microbenchmark and a slightly more realistic kernel. If both show the same trend, you can trust that the effect is not an artifact of the test harness.

How this fits on projects

You will use this concept to design the loop templates in §3.2 and to interpret the boundary in §3.7. It also informs the alignment decisions in §5.10 Phase 1.

Definitions & key terms

  • uOp cache -> storage for decoded micro-operations
  • decode bandwidth -> max uOps per cycle from decoders
  • uOp -> micro-operation derived from an ISA instruction
  • loop alignment -> placing loop start at a specific address boundary

Mental model diagram (ASCII)

Fetch bytes -> Decode -> uOps -> Backend
       \-> [uOp Cache Hit] -> uOps -> Backend

How it works (step-by-step, with invariants and failure modes)

  1. Fetch instruction bytes for loop.
  2. If uOp cache has the loop, deliver uOps directly.
  3. Otherwise, decode bytes into uOps and deliver them.
  4. Measure cycles per iteration across loop sizes.

Invariants:

  • uOp cache capacity is finite and measured in uOps.
  • Decode bandwidth is a hard limit when cache misses.

Failure modes:

  • Loop includes instructions not cached by uOp cache.
  • Loop exceeds L1I and confounds measurements.

Minimal concrete example

.align 64
loop:
  add rax, rbx
  add rcx, rdx
  add r8,  r9
  dec r10
  jnz loop

Common misconceptions

  • “uOp cache is the same as L1I” -> it stores decoded uOps, not bytes.
  • “All loops benefit” -> only loops that fit and are cacheable benefit.

Check-your-understanding questions

  1. Why does uOp cache capacity depend on uOps rather than bytes?
  2. What happens when a loop crosses a uOp cache line boundary?
  3. How do complex instructions affect uOp cache usage?

Check-your-understanding answers

  1. The cache stores decoded uOps, so capacity is in uOps.
  2. It may require multiple cache lines, reducing effective capacity.
  3. They expand into multiple uOps and consume capacity faster.

Real-world applications

  • Hot loop tuning in performance-critical code
  • Front-end optimization in compilers

Where you’ll apply it

References

  • “Inside the Machine” by Jon Stokes
  • “Agner Fog’s Optimization Manuals”

Key insights

  • uOp cache hits turn decode into a fast path; misses expose decode limits.

Summary

The uOp cache is a decoded instruction cache. Your benchmark detects its capacity by measuring when loop throughput drops.

Homework/Exercises to practice the concept

  1. Estimate how many uOps are in a 16-instruction loop of simple adds.
  2. Predict how doubling loop size affects uOp cache usage.

Solutions to the homework/exercises

  1. Simple adds are typically 1 uOp each, so about 16 uOps.
  2. Doubling loop size roughly doubles uOp usage and may cross capacity boundaries.

2.2 Loop Alignment and Instruction Fetch Effects

Fundamentals

Instruction fetch behavior depends on alignment and cache-line boundaries. The CPU fetches instruction bytes in fixed-size chunks, and decoder groups often prefer aligned boundaries. Misaligned loops can cause extra fetches or decoder inefficiency, which masks the effect you want to measure. By aligning the loop start and controlling loop size, you reduce noise and make uOp cache effects visible. Alignment is not just about correctness; it is about reproducible performance.

Additional fundamentals for Loop Alignment and Instruction Fetch Effects: focus on the simplest mental model and the most common unit of measurement. Identify what changes state, what observes that state, and which constraints are non-negotiable. This keeps the concept grounded before moving to deeper microarchitectural details.

Deep Dive into the concept

The instruction cache (L1I) is typically organized in 64-byte lines. The fetch unit pulls instruction bytes into a predecode queue. Decoders then parse instruction boundaries. If your loop straddles a cache line or a fetch block boundary, the front-end might need additional fetches or suffer from split decode windows. This can add a few cycles and show up as noise in tight loops. For a uOp cache experiment, you want the loop to behave consistently, so aligning the loop entry to 32 or 64 bytes is common.

Alignment also matters for the uOp cache, because its indexing is tied to instruction addresses. A loop that starts at an unlucky offset might map to the same cache set as another loop, causing conflict evictions. In your benchmark, you can avoid this by controlling loop address via .align and by placing the loop in its own section. Additionally, you can vary alignment intentionally to show its effect, which provides insight into front-end behavior.

Instruction fetch effects can mimic uOp cache boundaries. If you increase loop size and it starts to spill out of L1I, your cycles per iteration will increase, but that is a different phenomenon than uOp cache misses. Therefore, your measurement should scan loop sizes that are well below L1I capacity while still crossing uOp cache capacity. To isolate this, you can keep the loop body compact and use many iterations, then scale the number of unrolled instructions to control uOp count. Another useful trick is to add NOPs to increase byte size without increasing uOp count, helping you distinguish uOp cache effects from L1I fetch effects.

You must also consider branch predictor effects. The loop branch itself can be predicted perfectly, but if the loop structure changes (e.g., a longer unroll factor reduces the number of iterations), the predictor state may shift slightly. This is usually small but can appear as noise in very tight measurements. Keeping the loop structure consistent and only changing the unroll factor helps reduce this variability.

Finally, alignment affects not just average performance but variability. Misalignment can produce occasional slow iterations due to crossing boundaries at different points in the pipeline. When you see a noisy distribution, check alignment first. A well-aligned loop should produce stable, low-variance timing, which is essential for identifying the uOp cache boundary.

Additional deep dive considerations for Loop Alignment and Instruction Fetch Effects: In real designs, Loop Alignment and Instruction Fetch Effects is rarely isolated; it interacts with pipeline depth, power management, compiler decisions, and even microcode updates. When you study this behavior, vary one knob at a time and hold everything else constant: pin the core, fix the frequency if possible, warm up caches and predictors, and record the exact compiler flags. Vendor manuals describe typical behavior, but the actual thresholds can shift across steppings or microcode revisions, so empirical measurement is the ground truth. If your results disagree with published numbers, investigate confounders such as alignment, instruction form, address mapping, or hidden dependencies introduced by the compiler. From a software perspective, compilers and JITs implicitly target Loop Alignment and Instruction Fetch Effects via instruction selection, scheduling, and unrolling, so your measurements should be translated into actionable rules of thumb. Finally, validate with at least two workloads: a synthetic microbenchmark and a slightly more realistic kernel. If both show the same trend, you can trust that the effect is not an artifact of the test harness.

How this fits on projects

You will apply alignment rules when constructing loop templates in §5.2 and when interpreting anomalies in §7.1.

Definitions & key terms

  • alignment -> placing code at a specific byte boundary
  • fetch block -> fixed-size chunk of instruction bytes fetched per cycle
  • L1I -> level-1 instruction cache
  • unroll factor -> number of loop body repeats per iteration

Mental model diagram (ASCII)

[64B line] [64B line]
| loop start aligned | loop body fits | -> stable fetch

How it works (step-by-step, with invariants and failure modes)

  1. Align loop start to 64 bytes.
  2. Choose unroll factor to control uOp count.
  3. Keep total byte size below L1I threshold.
  4. Measure and record timing.

Invariants:

  • Alignment should be consistent across tests.
  • Loop body should be fixed except for unroll factor.

Failure modes:

  • Misaligned loop causes fetch stalls.
  • Loop spills out of L1I and hides uOp cache effects.

Minimal concrete example

.section .text
.align 64
loop:
  nop
  nop
  dec r10
  jnz loop

Common misconceptions

  • “Alignment only matters for data” -> instruction alignment also matters.
  • “NOPs are free” -> they still consume fetch bandwidth.

Check-your-understanding questions

  1. Why align the loop entry to 64 bytes?
  2. How can NOP padding help isolate uOp cache effects?
  3. What indicates L1I spill rather than uOp cache spill?

Check-your-understanding answers

  1. It aligns fetch and uOp cache indexing, reducing variability.
  2. It changes byte size without changing uOp count.
  3. A larger, more gradual slowdown and increased variance suggest L1I issues.

Real-world applications

  • Hot loop tuning in games and DSP code
  • JIT compilers that align code blocks

Where you’ll apply it

References

  • “Agner Fog’s Optimization Manuals” alignment sections
  • “Inside the Machine” by Jon Stokes

Key insights

  • Alignment is a performance control knob that stabilizes front-end measurements.

Summary

Loop alignment controls instruction fetch behavior. Without it, uOp cache measurements are noisy or misleading.

Homework/Exercises to practice the concept

  1. Compare the timing of aligned vs misaligned loops of identical size.
  2. Insert NOP padding and observe changes in timing.

Solutions to the homework/exercises

  1. Aligned loops show lower variance and often lower cycles per iteration.
  2. NOP padding increases byte size and can trigger fetch limits before uOp cache limits.

2.3 uOp Cache Set Mapping and Alignment Effects

Fundamentals

The uOp cache is not a flat bucket; it is organized in sets and ways much like the L1 cache. That means two loops with the same number of uOps can behave very differently depending on how their addresses map to uOp cache sets. Alignment determines which fetch blocks and cache sets the loop occupies, and boundary crossings can evict or disable uOp cache delivery. If you want to probe the uOp cache, you must treat alignment and code layout as first-class variables. A good prober changes alignment deliberately and records when throughput shifts from the uOp cache path to the legacy decoders. This teaches you that “size” alone does not explain uOp cache behavior; mapping and alignment matter just as much.

Deep Dive into the concept

On many modern x86 cores, the uOp cache (DSB) is indexed by instruction address and internally organized into sets with a fixed number of ways. Each entry stores a small sequence of decoded uOps corresponding to a block of instruction bytes. The mapping from instruction address to set is not public, but you can infer it by observing conflict behavior. For example, if two loops of the same size run fast individually but slow down when alternated, they may be thrashing the same uOp cache set. This is exactly the kind of behavior you can reveal with a prober.

Alignment matters because the front-end fetches in fixed-size chunks (often 16 or 32 bytes) and the uOp cache stores uOps in a structure keyed to those chunks. If a hot loop crosses a fetch boundary, it may require two uOp cache entries per iteration, cutting effective capacity in half. Similarly, if your loop straddles a 32-byte boundary that is special to the front-end, you can lose macro-fusion or force the legacy decoder path. These effects are subtle but measurable: one alignment can produce 4-6 uOps per cycle from the uOp cache, while a misaligned version falls back to 4-wide decode and shows lower IPC.

A good prober therefore varies alignment across a full cache line and records throughput at each offset. This produces an alignment sensitivity curve. You can also vary the number of uOps to find the largest loop that still fits entirely in the uOp cache. But to do that correctly, you must avoid confounding factors: keep the loop branch predictable, ensure the loop body is otherwise identical, and avoid large instruction sequences that spill into the instruction cache. The uOp cache is logically separate from the I-cache, but the front-end still fetches instruction bytes; I-cache misses can mask uOp cache effects.

To study set mapping, you can create multiple loops with the same size and then place them at different addresses using padding or linker scripts. Run them alternately and observe when performance degrades. If two loops conflict, alternating will cause uOp cache thrash, and you will see a drop in IPC or increase in front-end stalls. By sweeping offsets, you can infer the number of sets or the index function modulo some power of two. This is similar to reverse-engineering cache set mappings and is a useful microarchitecture exercise.

Finally, do not forget that uOp cache behavior interacts with macro-fusion. A fused pair reduces uOp count and may allow a loop to fit in the cache. A small change in instruction mix can therefore push a loop from “fits” to “spills”. Your prober should report both instruction count and uOp count, and it should offer a mode that disables fusion (for example, by inserting NOPs) to see how sensitive the cache is to fusion.

How this fits on projects

You will use this in §3.2 to define alignment controls, in §5.10 Phase 2 to sweep offsets, and in §7.3 to explain unexpected throughput cliffs.

Definitions & key terms

  • uOp cache set -> a group of entries that share an index
  • alignment -> the offset of code relative to fetch or cache boundaries
  • fetch block -> fixed-size chunk of instruction bytes fetched per cycle
  • thrashing -> repeated eviction caused by conflicts in a cache set
  • offset sweep -> scanning alignment offsets to measure sensitivity

Mental model diagram (ASCII)

Address -> Index -> [uOp cache set] -> ways
             ^ alignment controls index

How it works (step-by-step, with invariants and failure modes)

  1. Build a loop with a known uOp count and predictable branch.
  2. Sweep alignment offsets across a cache line.
  3. Measure throughput and front-end stalls per offset.
  4. Alternate multiple loops to induce potential conflicts.
  5. Infer capacity and set behavior from observed cliffs.

Invariants:

  • Loop body stays identical except for alignment padding.
  • Measurements are taken after warm-up.

Failure modes:

  • I-cache misses hide uOp cache effects.
  • Branch mispredictions dominate and mask front-end behavior.

Minimal concrete example

.align 64
loop:
  add rax, rbx
  add rcx, rdx
  dec rsi
  jnz loop

Common misconceptions

  • “uOp cache capacity is only about uOp count” -> mapping and alignment matter.
  • “Alignment only affects I-cache” -> it also changes uOp cache indexing and fusion.
  • “If IPC is high, uOp cache is irrelevant” -> it may be the reason IPC is high.

Check-your-understanding questions

  1. Why can two loops of equal size behave differently in the uOp cache?
  2. How does a 32-byte boundary affect macro-fusion?
  3. What measurement would indicate uOp cache thrashing?

Check-your-understanding answers

  1. They may map to different sets or require different numbers of uOp cache entries.
  2. It can split instruction pairs across fetch blocks, preventing fusion.
  3. Alternating two loops causes a drop in IPC and an increase in front-end stalls.

Real-world applications

  • JIT compilers aligning hot loops for uOp cache hits
  • Hand-tuned assembly for crypto or media kernels
  • Performance engineering for low-latency trading code

Where you’ll apply it

References

  • Intel Optimization Reference Manual, front-end chapters
  • Agner Fog, “The microarchitecture of Intel, AMD and VIA CPUs” (latest revision)

Key insights

  • Alignment is a performance parameter, not a cosmetic detail.

Summary

The uOp cache is a set-mapped structure influenced by alignment and loop layout. Measuring its behavior requires deliberate offset sweeps and conflict tests, not just counting uOps.

Homework/Exercises to practice the concept

  1. Sweep loop alignment in 16-byte increments and plot IPC vs offset.
  2. Create two loops of the same size and alternate them to detect conflicts.

Solutions to the homework/exercises

  1. IPC will show cliffs at certain offsets where uOp cache delivery degrades.
  2. Conflicting loops will show a drop in IPC when alternated.

3. Project Specification

3.1 What You Will Build

A microbenchmark that executes tight loops with controlled uOp counts and alignment. The tool measures cycles per iteration for each loop size and identifies the threshold at which the uOp cache stops serving the loop. It outputs a report with the estimated uOp cache capacity.

3.2 Functional Requirements

  1. Loop Generator: Emit loop bodies with configurable uOp counts and alignment.
  2. Timing Harness: Measure cycles per iteration with RDTSCP.
  3. Boundary Detection: Detect the point where cycles/iter increase sharply.
  4. Reporting: Output a table of size vs cycles and the inferred boundary.

3.3 Non-Functional Requirements

  • Performance: Measure 20 loop sizes in under 2 seconds total.
  • Reliability: Stable results across 5 trials.
  • Usability: CLI flags for alignment, unroll factor, and loop count.

3.4 Example Usage / Output

$ ./uop_cache_prober --max-uops 512 --align 64
size_uops,cycles_per_iter
64,1.1
128,1.1
256,1.2
320,2.4  <-- boundary

3.5 Data Formats / Schemas / Protocols

CSV output:

size_uops,cycles_per_iter
128,1.1
256,1.2
320,2.4

3.6 Edge Cases

  • Loop size too small to stabilize timing
  • Loop size so large it spills from L1I
  • Alignment values not supported by assembler

3.7 Real World Outcome

You will produce an estimated uOp cache capacity for your CPU and a graph of cycles per iteration vs uOp count.

3.7.1 How to Run (Copy/Paste)

as --64 -o loop.o loop.s
ld -o uop_cache_prober loop.o
./uop_cache_prober --max-uops 512 --align 64 --trials 5

3.7.2 Golden Path Demo (Deterministic)

  • Use fixed --align 64 --trials 5 and a fixed unroll factor.
  • Expect a clear step change in cycles/iter.

3.7.3 If CLI: Exact Terminal Transcript

$ ./uop_cache_prober --max-uops 512 --align 64
size_uops,cycles_per_iter
64,1.10
128,1.12
256,1.15
320,2.35

$ echo $?
0

Failure demo (bad alignment):

$ ./uop_cache_prober --align 48
Error: alignment must be power-of-two >= 16

$ echo $?
3

Exit codes:

  • 0 success
  • 2 assembler error
  • 3 invalid argument

4. Solution Architecture

4.1 High-Level Design

+-------------+   +------------------+   +-----------------+
| Loop Builder|-> | Timing Harness   |-> | Boundary Finder |
+-------------+   +------------------+   +-----------------+

4.2 Key Components

Component Responsibility Key Decisions
Loop Builder Emit aligned loop bodies Use simple cacheable ops
Harness Measure cycles/iter RDTSCP + fences
Analyzer Detect boundary Median + slope threshold

4.3 Data Structures (No Full Code)

struct Result { int uops; double cycles; };

4.4 Algorithm Overview

Key Algorithm: Boundary Detection

  1. Measure cycles/iter for each uOp count.
  2. Compute slope between successive points.
  3. Pick first slope above threshold as boundary.

Complexity Analysis:

  • Time: O(S) for S sizes
  • Space: O(S)

5. Implementation Guide

5.1 Development Environment Setup

as --version
ld --version

5.2 Project Structure

uop-cache-prober/
├── src/
│   ├── loop.s
│   ├── timing.c
│   └── main.c
└── README.md

5.3 The Core Question You’re Answering

“How big is the uOp cache on my CPU, really?”

Your measurement should show a clear boundary.

5.4 Concepts You Must Understand First

  1. uOp cache vs decode pipeline
  2. Loop alignment and L1I effects
  3. RDTSC-based timing

5.5 Questions to Guide Your Design

  1. How will you count uOps in your loop body?
  2. How will you ensure the loop stays in L1I?
  3. How will you detect the boundary reliably?

5.6 Thinking Exercise

Estimate the uOp count of a loop with 16 add instructions and one branch. How many uOps is it?

5.7 The Interview Questions They’ll Ask

  1. What is the difference between uOp cache and L1I?
  2. Why does loop alignment affect front-end throughput?
  3. How would you detect uOp cache capacity without counters?

5.8 Hints in Layers

Hint 1: Use only simple one-uOp instructions.

Hint 2: Align the loop to 64 bytes and keep size below L1I.

Hint 3: Plot cycles vs uOps to find the step change.

5.9 Books That Will Help

Topic Book Chapter
Front-end pipeline “Inside the Machine” Ch. 4
Microbenchmarking “Agner Fog” uOp cache section

5.10 Implementation Phases

Phase 1: Foundation (2-3 days)

  • Build aligned loop templates and compile them.
  • Checkpoint: loop runs with stable cycles.

Phase 2: Core Functionality (4-6 days)

  • Implement timing harness and collect results.
  • Checkpoint: data shows stable low cycles for small loops.

Phase 3: Analysis (2-3 days)

  • Detect and report boundary.
  • Checkpoint: boundary estimate produced.

5.11 Key Implementation Decisions

Decision Options Recommendation Rationale
Loop instructions NOP vs ADD ADD avoids zero-uOp issues
Boundary detection manual vs slope slope deterministic

6. Testing Strategy

6.1 Test Categories

Category Purpose Examples
Unit Tests Alignment logic align 64
Integration Tests End-to-end timing size 64-512
Edge Tests Very large loops size 2048

6.2 Critical Test Cases

  1. Aligned loop should be faster than misaligned loop.
  2. Small loops should show stable low cycles.
  3. Large loops should show a clear slowdown.

6.3 Test Data

size_uops: 64, 128, 256, 320

7. Common Pitfalls & Debugging

7.1 Frequent Mistakes

Pitfall Symptom Solution
Using complex instructions noisy results use simple ALU ops
L1I spill gradual slowdown keep size under L1I
Misalignment high variance add .align

7.2 Debugging Strategies

  • Inspect disassembly to count uOps.
  • Use perf to verify uOp cache hit/miss counters if available.

7.3 Performance Traps

  • Over-unrolling increases code size and confounds uOp cache measurement.

8. Extensions & Challenges

8.1 Beginner Extensions

  • Add a JSON output format.

8.2 Intermediate Extensions

  • Compare uOp cache boundary across different cores.

8.3 Advanced Extensions

  • Add BTB pressure tests to see interaction with uOp cache.

9. Real-World Connections

9.1 Industry Applications

  • JIT compiler code layout decisions
  • CPU front-end verification
  • llvm-mca: microarchitecture modeling
  • uops.info: curated uOp measurements

9.3 Interview Relevance

  • Demonstrates understanding of front-end pipeline and microbenchmarking.

10. Resources

10.1 Essential Reading

  • “Inside the Machine” by Jon Stokes
  • “Agner Fog’s Optimization Manuals”

10.2 Video Resources

  • “CPU Front-End and uOp Cache” lecture

10.3 Tools & Documentation

  • perf: validate uOp cache counters
  • objdump: confirm instruction alignment

11. Self-Assessment Checklist

11.1 Understanding

  • I can explain why uOp cache exists.
  • I can differentiate uOp cache vs L1I behavior.
  • I can justify alignment choices.

11.2 Implementation

  • The benchmark runs deterministically.
  • The boundary is clearly identified.
  • Results are stable across trials.

11.3 Growth

  • I can explain this method in a performance review.

12. Submission / Completion Criteria

Minimum Viable Completion:

  • Measure cycles/iter for at least 6 loop sizes.
  • Identify an estimated uOp cache boundary.

Full Completion:

  • Compare aligned vs misaligned loops.

Excellence (Going Above & Beyond):

  • Validate boundary with hardware counters.