Benchmark Details

Benchmark Details

This page describes the workloads used in each benchmark category. All tests are native Swift and run locally.

CPU Single-Core

Single-threaded CPU performance across mixed workloads.

Integer

• What: 64-bit integer arithmetic
• How: tight loop with add, multiply, shift, and XOR
• Metric: Mops/s (millions of operations per second)

Floating Point

• What: double-precision math
• How: multiply, sqrt, sin/cos operations
• Metric: Mops/s

SIMD (Accelerate)

• What: vectorized operations using vDSP
• How: vector multiply-add, vector add, dot product
• Metric: GFLOPS

Cryptography

• What: AES-256-GCM encryption + SHA-256 hashing
• How: encrypt a data buffer and hash ciphertext
• Metric: MB/s throughput

Compression

• What: LZFSE compression + decompression
• How: compress and decompress a buffer
• Metric: MB/s throughput (combined)

CPU Multi-Core

Same tests as single-core, executed in parallel across all available CPU cores.

• Uses Swift TaskGroup for parallel execution
• Totals throughput across tasks
• Score is normalized by core count (see Scoring Methodology)
• SIMD (v2.1.6+): Uses 8K-element vectors (~96KB per core) that fit in L1 cache, ensuring the test is compute-bound and scales properly with core count. Previous versions used 1M-element vectors (12MB) which caused memory bandwidth contention across cores.

Memory

Unified memory subsystem performance.

Sequential Read

• Linear read of a 256 MB buffer
• Page-aligned allocation with optional mlock
• Metric: GB/s

Sequential Write

• Linear write of a 256 MB buffer
• Page-aligned allocation with optional mlock
• Metric: GB/s

Copy

• memcpy between two 256 MB buffers
• Page-aligned allocation with optional mlock
• Metric: GB/s

Latency

• Pointer-chase random access
• 32 MB working set, 10M accesses
• Metric: ns (lower is better)

Note: Advanced profiling adds stride throughput and block-size sweeps for empirical cache boundary detection. Those sweeps report throughput vs stride/size, not latency.

Disk

Storage performance with cache bypass. Patterns are NovaBench-compatible for direct comparison.

Why NovaBench-Compatible?

We align with NovaBench patterns to enable meaningful cross-tool comparisons:

• Sequential: 4MB blocks (NovaBench uses up to 8 simultaneous)
• Random: 4KB blocks, QD1 (one operation at a time)
• All metrics in MB/s for consistency

Setup

• Creates a unique temp directory per run
• Uses O_NOFOLLOW to prevent symlink attacks
• Uses F_NOCACHE to bypass the filesystem cache
• Uses F_FULLFSYNC to force sync to physical media

Sequential Write

• 4 MB chunks into a 256 MB (quick) or 1 GB (normal) file
• Cache bypass + final sync
• Metric: MB/s

Sequential Read

• 4 MB chunks from a 256 MB (quick) or 1 GB (normal) file
• File is synced before reading to ensure cold read
• Metric: MB/s

Random Write

• 4 KB writes at random offsets in a 256 MB sparse file
• QD1 pattern (one I/O at a time)
• Metric: MB/s (converted from IOPS × 4KB / 1MB)

Random Read

• 4 KB reads at random offsets in a 256 MB file
• QD1 pattern (one I/O at a time)
• Metric: MB/s (converted from IOPS × 4KB / 1MB)

Disk Parameters

Parameter	Quick Mode	Normal Mode
Sequential file size	256 MB	1 GB
Sequential chunk size	4 MB	4 MB
Random file size	512 MB	1 GB
Random block size	4 KB	4 KB
Random operations	500	2,000

Disk I/O Volume (per iteration)

Disk tests run repeatedly for the selected duration, so total bytes scale with duration and drive speed. Per iteration of each subtest:

• Sequential write/read: writes/reads the sequential file size (256 MB quick / 1 GB normal) in 4 MB blocks, then syncs
• Random write: 500/2,000 operations of 4 KB each (about 2 MB / 8 MB total), then final sync
• Random read: 500/2,000 operations of 4 KB each (about 2 MB / 8 MB total); each iteration also creates and syncs a 512 MB / 1 GB file to keep reads cold

Cache bypass is best-effort; macOS VM and SSD controller caches can still influence results.

Comparison with NovaBench

Metric	Our Tool (M1)	NovaBench M1	Why Different
Seq Read	~2180 MB/s	3356 MB/s	We use F_NOCACHE
Seq Write	~700 MB/s	3279 MB/s	We use F_FULLFSYNC
Rand Read	~43 MB/s	166 MB/s	1GB file exceeds cache
Rand Write	~17 MB/s	761 MB/s	F_NOCACHE + final sync

Note: Our tool measures actual disk performance (what you'd see in real workloads), not cache throughput. NovaBench numbers include filesystem cache effects, which makes them higher but less representative of sustained I/O performance.

GPU (Metal)

Integrated GPU compute benchmarks using Metal.

Compute (Matrix Multiply)

• Dense matrix multiplication
• Size: 1024x1024 (quick) or 2048x2048 (normal)
• Metric: GFLOPS

Particles Simulation

• N-body style simulation
• Particles: 100,000 (quick) or 1,000,000 (normal)
• Metric: Mparts/s

Gaussian Blur

• 5x5 convolution on an image
• Size: 2048x2048 (quick) or 4096x4096 (normal)
• Metric: MP/s

Edge Detection (Sobel)

• Sobel filter on the same image size
• Metric: MP/s

GPU Parameters

Parameter	Quick Mode	Normal Mode
Matrix size	1024×1024	2048×2048
Particle count	100,000	1,000,000
Image size (blur/edge)	2048×2048	4096×4096

GPU Notes

• Metal shaders are compiled at runtime from inline source (SPM compatible)
• Texture data is heap-allocated to avoid stack overflow on large images (v2.1.1+)
• Uses deterministic patterns for reproducibility
• If Metal is unavailable, GPU tests return 0 and are scored as "Failed"

AI/ML (v2.0.0+)

Neural Engine and machine learning inference benchmarks using CoreML and Accelerate.

Why a Separate AI Score?

The AI/ML score is not included in the Total Score. This mirrors Geekbench AI's approach:

• AI workloads have different characteristics than traditional benchmarks
• Neural Engine performance varies significantly between chip generations
• Users may care about AI performance independently from general compute

CoreML Inference Tests

All CoreML tests use the same model (MobileNetV2 image classification) with different compute units:

CPU Inference

• What: CoreML inference using CPU-only
• Compute Units: .cpuOnly
• Metric: IPS (inferences per second)
• Measures: CPU's ability to run ML models without GPU/ANE

GPU Inference

• What: CoreML inference using GPU
• Compute Units: .cpuAndGPU
• Metric: IPS (inferences per second)
• Measures: GPU's ability to accelerate ML inference

Neural Engine Inference

• What: CoreML inference with Neural Engine
• Compute Units: .all (CoreML schedules to ANE when beneficial)
• Metric: IPS (inferences per second)
• Measures: ANE throughput for supported operations

BNNS (Accelerate Framework)

• What: Matrix multiplication using vDSP
• Size: 512×512 (quick) or 1024×1024 (normal)
• Metric: GFLOPS
• Measures: CPU vector math performance for ML workloads

AI Model Management

The benchmark downloads a CoreML model from Apple's official ML assets on first run:

• Model: MobileNetV2 image classification (~17MB source)
• Source: ml-assets.apple.com (Apple's official CoreML model repository)
• Compilation: Compiled locally using CoreML framework (no Xcode required)
• Cache: ~/Library/Application Support/osx-bench/models/
• Offline mode: Use --offline to skip if model not cached
• Custom model: Use --model-path for local .mlmodelc models

AI Test Parameters

Parameter	Quick Mode	Normal Mode
Warmup iterations	5	20
Min iterations	5	10
Max iterations	1,000	10,000
BNNS matrix size	512×512	1024×1024

AI Notes

• Neural Engine availability depends on macOS version and chip
• CoreML decides layer scheduling - not all operations run on ANE even with .all
• Input is synthetic (deterministic pattern for reproducibility)
• Results comparable to Geekbench AI methodology (see Geekbench AI Workloads paper)

Thermal Monitoring

Thermal state is tracked during a run using the macOS public API:

• Nominal
• Fair
• Serious
• Critical

If throttling is detected, scores may be lower than peak performance.

Duration Modes

Mode	Flag	Duration	Use Case
Quick	--quick	~3s per category	Fast iteration
Normal	default	10s per category	Standard runs
Custom	-d N	N seconds	Tuned runs
Stress	--stress	~60s per category	Sustained performance
Repeat	--repeats N	N × full run	Publishable results (median)