perf_analysis.md

CPU-Level Performance Analysis: Why scanner-rs Is Faster

Generated: 2026-02-15 23:35:37 UTC

1. Test Configuration

Parameter	Value
Repo	vscode (git mode, warm cache)
Bytes scanned	1,203,352,576 (1.12 GiB)
Machine	ARM Graviton3 (aarch64), 16 vCPUs, 61 GiB RAM
perf_event_paranoid	2 (user-space events, :u suffix)
Methodology	1 warmup + 1 measured run per (scanner, event group)
Event multiplexing	6 events per group, time-multiplexed by kernel

Original benchmark results (vscode, git, warm):

Scanner	Wall Time	Throughput	Findings
scanner-rs	13.7s	84 MiB/s	98,584
Kingfisher	31.1s	37 MiB/s	303
TruffleHog	178.5s	6.4 MiB/s	0
Gitleaks	113.7s	10.1 MiB/s	116

2. Raw Counters

Metric	scanner-rs	Kingfisher	TruffleHog	Gitleaks
Cycles	157,619,084,107	532,509,784,422	678,210,237,914	2,271,669,688,468
Instructions	411,789,892,684	1,426,719,985,424	1,696,903,305,992	10,690,140,886,959
L1D loads	123,210,234,217	452,557,253,455	498,049,596,053	4,452,278,795,146
L1D misses	1,209,415,888	2,447,094,121	5,128,182,208	8,775,249,857
L1I loads	80,809,948,119	330,390,689,117	355,390,878,506	1,523,868,766,465
L1I misses	283,561,600	745,428,529	5,392,181,661	2,888,133,830
L2D refill	328,843,938	497,284,829	2,618,442,067	1,850,602,731
L2D LLC miss	329,841,031	495,869,342	2,592,819,199	1,843,850,762
L2D writeback	713,524,135	1,517,956,160	4,007,442,708	3,145,739,388
L2D allocate	208,956,309	474,052,300	620,612,044	524,963,070
Memory accesses	123,985,681,355	454,409,357,352	499,102,529,628	4,445,246,559,947
Branch predictions	97,941,399,259	299,916,874,655	309,692,700,357	2,621,171,103,819
Branch misses	1,870,945,056	8,238,677,792	7,879,836,883	11,553,796,741
Frontend stalls	11,626,952,340	52,555,189,605	97,980,832,540	90,284,295,847
Backend stalls	64,097,407,942	141,440,571,268	230,734,465,747	663,045,657,519
dTLB loads	126,957,811,310	452,810,087,792	499,657,825,644	4,455,730,287,796
dTLB misses	826,966,710	1,533,221,981	4,008,936,174	4,157,538,969
dTLB walks	78,874,945	111,265,937	461,329,206	283,306,516
iTLB loads	25,600,430,196	143,805,579,347	167,599,942,712	167,910,816,063
iTLB misses	44,955,625	112,654,236	879,805,524	415,683,618

3. Derived Metrics Comparison

Metric	scanner-rs	Kingfisher	TruffleHog	Gitleaks
IPC	2.61	2.68	2.50	4.71
L1D miss rate	0.982%	0.541%	1.03%	0.197%
L1I miss rate	0.351%	0.226%	1.52%	0.190%
L2 miss rate	6.24%	4.27%	9.55%	6.42%
LLC miss rate	6.25%	4.26%	9.46%	6.40%
Branch miss rate	1.91%	2.75%	2.54%	0.441%
Frontend stall	7.38%	9.87%	14.45%	3.97%
Backend stall	40.67%	26.56%	34.02%	29.19%
dTLB miss rate	0.651%	0.339%	0.802%	0.093%
iTLB miss rate	0.176%	0.078%	0.525%	0.248%
Insns per L1D miss	340.49	583.03	330.90	1,218.21
Bytes per insn	0.0029	0.0008	0.0007	0.0001

4. Architecture Deep-Dive

Each subsection maps a measured CPU-level advantage to specific design decisions in scanner-rs source code, contrasting with the approach taken by Go-based competitors.

4.1 Vectorscan Multi-Pattern DFA → Higher IPC, Fewer Branch Misses

Measured:

Metric	scanner-rs	Kingfisher	TruffleHog	Gitleaks
IPC	2.61	2.68	2.50	4.71
Branch misses	1,870,945,056	8,238,677,792	7,879,836,883	11,553,796,741
Branch miss rate	1.91%	2.75%	2.54%	0.441%

Design: scanner-rs compiles all ~223 detection rules into a single Vectorscan (Hyperscan) multi-pattern DFA that scans the input buffer in one pass using SIMD-accelerated state transitions. The DFA's deterministic state machine eliminates per-pattern branch speculation — each byte advances the state via a table lookup rather than a branch tree.

Note on IPC: Gitleaks shows higher IPC because its inner loop (iterate rules, run regex, check match) is a simple, predictable pattern that the branch predictor handles well. However, it executes 26× more instructions — high IPC on wasted work is not an advantage. scanner-rs executes complex DFA state transitions (lower IPC per instruction) but needs 3.5–26× fewer total instructions, resulting in 3.4–14.4× fewer total cycles.

Code:

• src/engine/vectorscan_prefilter.rs:112-135 — VsPrefilterDb: compiled database holding all patterns
• src/engine/vectorscan_prefilter.rs:89-100 — RawPatternMeta: 12-byte #[repr(C)] per-pattern metadata for the match callback
• src/engine/core.rs:30-44 — Scan phase algorithm: prefilter → window → validate

Contrast: TruffleHog and Gitleaks are written in Go. TruffleHog uses a per-detector regex scan loop — each detector runs its own regex engine against every matched span. Gitleaks iterates all rules sequentially with Go's regexp package. Both approaches create unpredictable branching as the CPU must speculate which rule will match next. Kingfisher (also Rust) uses Vectorscan + per-rule regex — the same two-stage architecture — but without scanner-rs's window narrowing, resulting in separate regex validation over broader regions.

4.2 Per-Worker Scratch Memory → Lower L2 Cache Refills

Measured:

Metric	scanner-rs	Kingfisher	TruffleHog	Gitleaks
L2 refills	328,843,938	497,284,829	2,618,442,067	1,850,602,731
L2 miss rate	6.24%	4.27%	9.55%	6.42%
LLC misses	329,841,031	495,869,342	2,592,819,199	1,843,850,762

Design: Each scanner-rs worker thread owns its own WorkerCtx containing a ScratchVec, Vectorscan VsScratch, and BufferPool — all accessed via Rc (not Arc), never shared across threads. This means each worker's hot data stays in its own L1/L2 cache slice without cross-core invalidation traffic.

Code:

• src/scratch_memory.rs:54 — ScratchVec<T>: fixed-capacity, page-aligned, never reallocates
• src/scheduler/executor.rs:472-508 — WorkerCtx: per-worker deque + scratch + metrics
• src/engine/vectorscan_prefilter.rs:229-252 — VsScratch: per-thread, Send but not Sync

Contrast: Go scanners share detector state behind mutexes or use goroutine-local state that the Go runtime may migrate between OS threads. This migration causes cache-line bouncing as L2 lines must be fetched from remote cores. The MOESI/MESI protocol upgrade from Shared→Modified on every counter increment amplifies this cost.

4.3 Cache-Line Aligned Atomics → No False Sharing

Measured:

Metric	scanner-rs	Kingfisher	TruffleHog	Gitleaks
L2 writebacks	713,524,135	1,517,956,160	4,007,442,708	3,145,739,388
L2 allocations	208,956,309	474,052,300	620,612,044	524,963,070

Design: Scanner-rs uses #[repr(align(64))] padding on shared atomic counters (CachePaddedAtomicU64) to ensure each counter occupies its own 64-byte cache line. A compile-time assertion verifies the size and alignment. This eliminates false sharing where incrementing one counter would invalidate adjacent counters in the same cache line.

Code:

• src/engine/core.rs:142-167 — CachePaddedAtomicU64 with #[repr(align(64))] and compile-time size/alignment assertion
• src/engine/core.rs:172-179 — VectorscanCounters: each field is a CachePaddedAtomicU64

Contrast: In Go, atomic.Int64 fields are typically packed together in structs without explicit padding. When multiple goroutines increment adjacent counters, the cache-line ping-pong effect causes L2 writebacks to spike as modified lines bounce between cores.

4.4 Pre-Allocated Fixed-Capacity Structures → Lower dTLB Misses

Measured:

Metric	scanner-rs	Kingfisher	TruffleHog	Gitleaks
dTLB misses	826,966,710	1,533,221,981	4,008,936,174	4,157,538,969
dTLB miss rate	0.651%	0.339%	0.802%	0.093%
dTLB walks	78,874,945	111,265,937	461,329,206	283,306,516

Design: scanner-rs pre-allocates all major data structures at startup and never reallocates during scanning:

• ScratchVec: page-aligned, fixed capacity, no reallocation
• NodePoolType: contiguous arena with bitset free-list
• BufferPool: pre-allocated fixed-size (8 MiB) chunk buffers

These allocations form a compact, stable virtual address footprint. The TLB entries for these pages stay warm throughout the scan because the same pages are accessed repeatedly without new mappings.

Code:

• src/scratch_memory.rs:43-127 — ScratchVec<T>: page-aligned, never grows
• src/pool/node_pool.rs:44-114 — NodePoolType: contiguous buffer + bitset free-list, O(1) allocate/free
• src/runtime.rs:570-704 — BufferPoolInner: Rc+UnsafeCell, single-threaded, fixed capacity

Contrast: Go's garbage collector periodically moves objects, which invalidates TLB entries for the relocated pages. Dynamic append() on slices triggers reallocation and copies to new virtual addresses, further fragmenting the address space and increasing TLB pressure.

4.5 Compact Packed Metadata → Better L1 Cache Density

Measured:

Metric	scanner-rs	Kingfisher	TruffleHog	Gitleaks
Insns per L1D miss	340.49	583.03	330.90	1,218.21
L1D miss rate	0.982%	0.541%	1.03%	0.197%

Design: Hot-path metadata is packed into minimal, cache-friendly structures:

• PairMeta: 4 bytes (u16 len + u8 coalesced + u8 pad). 16 consecutive pairs fit in one 64-byte cache line.
• RawPatternMeta: 12 bytes (u32 rule_id + u32 match_width + u32 seed_radius), #[repr(C)] with a compile-time size assertion. 5 entries fit per cache line.

Code:

• src/engine/hit_pool.rs:82-101 — PairMeta (4 bytes, #[repr(C)])
• src/engine/vectorscan_prefilter.rs:89-100 — RawPatternMeta (12 bytes, #[repr(C)], compile-time size guard)

Contrast: Go interface values carry a 16-byte header (type pointer + data pointer). Each regexp.Regexp object includes multiple pointer-chased fields. A detector list of 223 interface values occupies ~3.5 KiB of headers alone — over 50 cache lines — before any pattern data is touched. scanner-rs packs the equivalent metadata into ~2.6 KiB (223 × 12 bytes) with guaranteed sequential layout.

4.6 Work-Stealing Scheduler → Lower Backend Stalls

Measured:

Metric	scanner-rs	Kingfisher	TruffleHog	Gitleaks
Backend stall %	40.67%	26.56%	34.02%	29.19%
Frontend stall %	7.38%	9.87%	14.45%	3.97%

Design: scanner-rs uses a custom work-stealing executor with Chase-Lev deques (LIFO local push/pop, FIFO steal) and a tiered idle strategy (spin → yield → park). Local-first spawn maximizes cache locality; randomized stealing avoids correlated contention. Workers park with a 200μs timeout to balance responsiveness against CPU waste.

Note on stall rates: scanner-rs shows a higher backend stall percentage because its Vectorscan DFA is memory-bandwidth bound — it streams data through the cache hierarchy at maximum throughput, causing the execution units to occasionally wait for data. However, in absolute stall cycles, scanner-rs has 2.2× fewer backend stalls than Kingfisher and 3.6× fewer than TruffleHog. The high stall rate reflects efficient memory-bound work, not wasted cycles.

Code:

• src/scheduler/executor.rs:3-54 — Architecture diagram and design docs
• src/scheduler/executor.rs:74-142 — ExecutorConfig with tuning knobs
• src/scheduler/executor.rs:472-508 — WorkerCtx: per-worker deque, scratch, RNG for steal-victim selection

Contrast: Go's runtime scheduler uses a global run queue (GRQ) plus per-P local run queues. Goroutines can be migrated between OS threads by the runtime, causing unpredictable cache invalidation. The Go scheduler also lacks backpressure — it will eagerly create goroutines that sit idle waiting for I/O, consuming stack memory and scheduling overhead.

4.7 Anchor-First Scanning → Fewer Total Instructions

Measured:

Metric	scanner-rs	Kingfisher	TruffleHog	Gitleaks
Total instructions	411,789,892,684	1,426,719,985,424	1,696,903,305,992	10,690,140,886,959
Kingfisher / scanner-rs ratio	—	3.46×	—	—
TruffleHog / scanner-rs ratio	—	—	4.12×	—
Gitleaks / scanner-rs ratio	—	—	—	25.96×

Design: scanner-rs uses an anchor-first scanning strategy. The Vectorscan prefilter identifies literal anchor hits in a single SIMD pass. Only the narrow windows around anchor hits are fed to the full regex engine for validation. Most of the input buffer is never touched by regex at all.

Code:

• src/engine/vectorscan_prefilter.rs — Prefilter DB compilation and match callback
• src/engine/core.rs:30-44 — Scan algorithm: prefilter seeds windows, regex only runs in hit windows
• src/engine/buffer_scan.rs — Window validation: regex runs only within seeded window bounds

Contrast: TruffleHog runs each detector's regex against every matched span, performing O(rules × spans) regex work. Gitleaks iterates all rules sequentially against the full input. Both approaches execute orders of magnitude more instructions because they cannot skip non-matching regions.

5. Summary: Design Decision → Metric → Impact

Key insight: Per-instruction rates (L1D miss rate, branch miss rate) can be misleading when comparing scanners that execute vastly different amounts of work. Gitleaks executes 26× more instructions than scanner-rs on the same input — a simple loop pattern yields good per-instruction rates but terrible absolute performance. The table below uses absolute counts which directly determine wall-clock time.

#	Design Decision	Key Metric	scanner-rs	Closest Competitor	Advantage
1	Vectorscan multi-pattern DFA	Total cycles	157,619,084,107	Kingfisher: 532,509,784,422	3.4× fewer
2	Anchor-first scanning	Total instructions	411,789,892,684	Kingfisher: 1,426,719,985,424	3.5× fewer
3	Deterministic DFA transitions	Branch misses	1,870,945,056	TruffleHog: 7,879,836,883	4.2× fewer
4	Per-worker scratch (no sharing)	L2 refills	328,843,938	Kingfisher: 497,284,829	1.5× fewer
5	Compact packed metadata	L1D misses	1,209,415,888	Kingfisher: 2,447,094,121	2.0× fewer
6	Pre-allocated fixed-capacity pools	dTLB misses	826,966,710	Kingfisher: 1,533,221,981	1.9× fewer
7	Work-stealing + cache locality	Backend stall cycles	64,097,407,942	Kingfisher: 141,440,571,268	2.2× fewer
8	Cache-line aligned atomics	L2 writebacks	713,524,135	Kingfisher: 1,517,956,160	2.1× fewer

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Report generated by tools/bench-compare/analyze_perf.py from perf stat measurements on ARM Graviton3.