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workload_profiles

sc_crawler.workload_profiles #

Workload profile definitions for compound benchmark scoring.

Each workload profile is a weighted combination of benchmark scores that represents a specific real-world usage pattern. Scores are normalised to [0, 1] across all servers and aggregated as a weighted mean.

Weights within each workload sum to 1.0.

Classes:

Name Description
BenchmarkEntry

A single benchmark component contributing to a workload profile score.
Workload

A named workload profile composed of weighted benchmark entries.

Attributes:

Name Type Description
WORKLOADS dict[str, Workload]

Workload profile definitions keyed by workload ID.

BenchmarkEntry #

Bases: BaseModel

A single benchmark component contributing to a workload profile score.

Attributes:

Name Type Description
benchmark_id str

The benchmark ID of a BenchmarkScore.
weight float

Relative weight of this component. Weights within a workload sum to 1.0.
label str

Human-readable description of what this component measures.
config_filter dict[str, Any] | None

Optional filter applied to the benchmark's config JSON column.
Source code in sc_crawler/workload_profiles.py 
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class BenchmarkEntry(BaseModel):
    """A single benchmark component contributing to a workload profile score."""

    benchmark_id: str
    """The benchmark ID of a BenchmarkScore."""
    weight: float
    """Relative weight of this component. Weights within a workload sum to 1.0."""
    label: str
    """Human-readable description of what this component measures."""
    config_filter: dict[str, Any] | None = None
    """Optional filter applied to the benchmark's config JSON column."""

    model_config = ConfigDict(frozen=True)

benchmark_id instance-attribute #

benchmark_id

The benchmark ID of a BenchmarkScore.

weight instance-attribute #

weight

Relative weight of this component. Weights within a workload sum to 1.0.

label instance-attribute #

label

Human-readable description of what this component measures.

config_filter class-attribute instance-attribute #

config_filter = None

Optional filter applied to the benchmark's config JSON column.

Workload #

Bases: BaseModel

A named workload profile composed of weighted benchmark entries.

Attributes:

Name Type Description
name str

Short human-readable name, e.g. 'Web server'.
version str

Workload profile version.
rationale str

Explanation of which benchmarks were chosen and why.
benchmarks list[BenchmarkEntry]

Ordered list of benchmark components with weights.
Source code in sc_crawler/workload_profiles.py 
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class Workload(BaseModel):
    """A named workload profile composed of weighted benchmark entries."""

    name: str
    """Short human-readable name, e.g. 'Web server'."""
    version: str
    """Workload profile version."""
    rationale: str
    """Explanation of which benchmarks were chosen and why."""
    benchmarks: list[BenchmarkEntry]
    """Ordered list of benchmark components with weights."""

name instance-attribute #

name

Short human-readable name, e.g. 'Web server'.

version instance-attribute #

version

Workload profile version.

rationale instance-attribute #

rationale

Explanation of which benchmarks were chosen and why.

benchmarks instance-attribute #

benchmarks

Ordered list of benchmark components with weights.

WORKLOADS module-attribute #

WORKLOADS = {'web': Workload(name='Web Server', version='1.0', rationale='Primary workloads drivers are HTTP serving speed and throughput, HTML and text processing, TLS termination, and asset compression.', benchmarks=[BenchmarkEntry(benchmark_id='static_web:rps-extrapolated', weight=0.25, label='Static web RPS (1 kB, 8 conn/vCPU)', config_filter={'size': '1k', 'connections_per_vcpus': 8.0}), BenchmarkEntry(benchmark_id='static_web:rps-extrapolated', weight=0.15, label='Static web RPS (64 kB, 8 conn/vCPU)', config_filter={'size': '64k', 'connections_per_vcpus': 8.0}), BenchmarkEntry(benchmark_id='static_web:throughput-extrapolated', weight=0.15, label='Static web throughput (256 kB, 8 conn/vCPU)', config_filter={'size': '256k', 'connections_per_vcpus': 8.0}), BenchmarkEntry(benchmark_id='geekbench:html5_browser', weight=0.1, label='Geekbench HTML5 browser (multi-core)', config_filter={'cores': 'multi'}), BenchmarkEntry(benchmark_id='geekbench:text_processing', weight=0.05, label='Geekbench text processing (multi-core)', config_filter={'cores': 'multi'}), BenchmarkEntry(benchmark_id='openssl', weight=0.1, label='OpenSSL AES-256-CBC (16 kB blocks)', config_filter={'algo': 'AES-256-CBC', 'block_size': 16384}), BenchmarkEntry(benchmark_id='passmark:cpu_encryption_test', weight=0.1, label='PassMark encryption (AES/SHA/ECDSA)'), BenchmarkEntry(benchmark_id='passmark:cpu_string_sorting_test', weight=0.05, label='PassMark string sorting'), BenchmarkEntry(benchmark_id='passmark:memory_read_cached', weight=0.05, label='PassMark cached memory reads')]), 'compute': Workload(name='Compute Heavy Applications', version='1.0', rationale='Number-crunching workload augmenting raw CPU performance stressing, general CPU performance benchmarks, memory bandwidth, and pure math computation speed like floating point, integer, SIMD (AVX/SSE/FMA) operations.', benchmarks=[BenchmarkEntry(benchmark_id='stress_ng:bestn', weight=0.15, label='stress-ng div16 best-N cores'), BenchmarkEntry(benchmark_id='stress_ng:best1', weight=0.1, label='stress-ng div16 single core'), BenchmarkEntry(benchmark_id='passmark:cpu_mark', weight=0.1, label='PassMark CPU Mark (composite)'), BenchmarkEntry(benchmark_id='geekbench:score', weight=0.1, label='Geekbench score (multi-core)', config_filter={'cores': 'multi'}), BenchmarkEntry(benchmark_id='bw_mem', weight=0.1, label='Memory bandwidth (read, 64 MB)', config_filter={'operation': 'rd', 'size': 64.0}), BenchmarkEntry(benchmark_id='passmark:cpu_floating_point_maths_test', weight=0.15, label='PassMark floating point'), BenchmarkEntry(benchmark_id='passmark:cpu_extended_instructions_test', weight=0.15, label='PassMark AVX/SSE/FMA (SIMD)'), BenchmarkEntry(benchmark_id='passmark:cpu_integer_maths_test', weight=0.1, label='PassMark integer math'), BenchmarkEntry(benchmark_id='passmark:cpu_physics_test', weight=0.05, label='PassMark physics simulation')]), 'cache': Workload(name='Cache Intensive', version='1.0', rationale='In-memory key-value store workload, mixing direct Redis performance metrics with memory speed and latency benchmarks, and single-core CPU performance profiles.', benchmarks=[BenchmarkEntry(benchmark_id='redis:rps-extrapolated', weight=0.25, label='Redis RPS (pipeline=1, SET)', config_filter={'operation': 'SET', 'pipeline': 1.0}), BenchmarkEntry(benchmark_id='redis:rps-extrapolated', weight=0.1, label='Redis RPS (pipeline=16, SET)', config_filter={'operation': 'SET', 'pipeline': 16.0}), BenchmarkEntry(benchmark_id='redis:latency', weight=0.1, label='Redis latency (pipeline=1, SET)', config_filter={'operation': 'SET', 'pipeline': 1.0}), BenchmarkEntry(benchmark_id='passmark:memory_latency', weight=0.1, label='PassMark memory latency (512 MB)'), BenchmarkEntry(benchmark_id='passmark:memory_mark', weight=0.1, label='PassMark Memory Mark (composite)'), BenchmarkEntry(benchmark_id='passmark:memory_read_cached', weight=0.1, label='PassMark cached memory reads'), BenchmarkEntry(benchmark_id='bw_mem', weight=0.1, label='Memory bandwidth (read, 16 MB ~ L3)', config_filter={'operation': 'rd', 'size': 16.0}), BenchmarkEntry(benchmark_id='passmark:cpu_single_threaded_test', weight=0.1, label='PassMark single-thread CPU'), BenchmarkEntry(benchmark_id='passmark:database_operations', weight=0.05, label='PassMark in-memory DB operations')]), 'database': Workload(name='Relational Database', version='1.0', rationale='Relational database workload (PostgreSQL, MySQL, transactional OLTP). Direct DB operation throughput is the primary driver, followed by memory latency for index lookups and buffer pool access, memory subsystem performance for working-set throughput, and single-thread CPU for query execution.', benchmarks=[BenchmarkEntry(benchmark_id='passmark:database_operations', weight=0.35, label='PassMark in-memory DB operations'), BenchmarkEntry(benchmark_id='passmark:memory_latency', weight=0.3, label='PassMark memory latency (512 MB)'), BenchmarkEntry(benchmark_id='passmark:memory_mark', weight=0.2, label='PassMark Memory Mark (composite)'), BenchmarkEntry(benchmark_id='passmark:cpu_single_threaded_test', weight=0.15, label='PassMark single-thread CPU')]), 'data_analysis': Workload(name='Data Analysis', version='1.0', rationale='Data analysis and ETL workloads are memory-bandwidth-bound and CPU-throughput-driven. The profile combines general CPU performance and memory bandwidth/latency as the primary drivers, supplemented by single-core compression speed as a proxy for serialisation-heavy ETL tasks.', benchmarks=[BenchmarkEntry(benchmark_id='passmark:cpu_mark', weight=0.3, label='PassMark CPU Mark (composite)'), BenchmarkEntry(benchmark_id='bw_mem', weight=0.3, label='Memory bandwidth (read, 64 MB)', config_filter={'operation': 'rd', 'size': 64.0}), BenchmarkEntry(benchmark_id='passmark:memory_mark', weight=0.25, label='PassMark Memory Mark (composite)'), BenchmarkEntry(benchmark_id='compression_text:compress', weight=0.15, label='Gzip compression (single-thread, level 5)', config_filter={'algo': 'gzip', 'compression_level': 5, 'cores': 'single'})]), 'llm': Workload(name='Multimodal LLM Inference', version='1.0', rationale='VRAM and memory-bandwidth-bound LLM inference workload, using direct LLM speed benchmarks at two model sizes, and supplementing with raw memory bandwidth, SIMD, and Geekbench computer vision workloads that exercise ML-style pipelines.', benchmarks=[BenchmarkEntry(benchmark_id='llm_speed:text_generation', weight=0.2, label='LLM text generation (llama-7b, 128 tok)', config_filter={'model': 'llama-7b.Q4_K_M.gguf', 'tokens': 128}), BenchmarkEntry(benchmark_id='llm_speed:prompt_processing', weight=0.1, label='LLM prompt processing (llama-7b, 512 tok)', config_filter={'model': 'llama-7b.Q4_K_M.gguf', 'tokens': 512}), BenchmarkEntry(benchmark_id='llm_speed:text_generation', weight=0.1, label='LLM text generation (gemma-2b, 128 tok)', config_filter={'model': 'gemma-2b.Q4_K_M.gguf', 'tokens': 128}), BenchmarkEntry(benchmark_id='passmark:memory_mark', weight=0.1, label='PassMark Memory Mark (composite)'), BenchmarkEntry(benchmark_id='bw_mem', weight=0.15, label='Memory bandwidth (read, 256 MB)', config_filter={'operation': 'rd', 'size': 256.0}), BenchmarkEntry(benchmark_id='passmark:cpu_extended_instructions_test', weight=0.1, label='PassMark AVX/SSE/FMA (SIMD)'), BenchmarkEntry(benchmark_id='passmark:cpu_floating_point_maths_test', weight=0.05, label='PassMark floating point'), BenchmarkEntry(benchmark_id='geekbench:object_detection', weight=0.1, label='Geekbench object detection (multi-core)', config_filter={'cores': 'multi'}), BenchmarkEntry(benchmark_id='geekbench:background_blur', weight=0.05, label='Geekbench background blur (multi-core)', config_filter={'cores': 'multi'}), BenchmarkEntry(benchmark_id='geekbench:structure_from_motion', weight=0.05, label='Geekbench structure-from-motion (multi-core)', config_filter={'cores': 'multi'})]), 'cicd': Workload(name='CI/CD Build', version='1.0', rationale='Build performance is driven by single- and multi-core compilation throughput, single-core CPU performance, multi-core compression and text/scripting processing.', benchmarks=[BenchmarkEntry(benchmark_id='geekbench:clang', weight=0.25, label='Geekbench Clang compilation (multi-core)', config_filter={'cores': 'multi'}), BenchmarkEntry(benchmark_id='geekbench:clang', weight=0.1, label='Geekbench Clang compilation (single-core)', config_filter={'cores': 'single'}), BenchmarkEntry(benchmark_id='passmark:cpu_single_threaded_test', weight=0.15, label='PassMark single-thread CPU'), BenchmarkEntry(benchmark_id='passmark:cpu_compression_test', weight=0.1, label='PassMark compression'), BenchmarkEntry(benchmark_id='passmark:cpu_integer_maths_test', weight=0.1, label='PassMark integer math'), BenchmarkEntry(benchmark_id='geekbench:text_processing', weight=0.1, label='Geekbench text processing (multi-core)', config_filter={'cores': 'multi'}), BenchmarkEntry(benchmark_id='stress_ng:bestn', weight=0.05, label='stress-ng div16 best-N cores'), BenchmarkEntry(benchmark_id='geekbench:file_compression', weight=0.05, label='Geekbench file compression (multi-core)', config_filter={'cores': 'multi'}), BenchmarkEntry(benchmark_id='compression_text:compress', weight=0.05, label='Brotli compression (single-thread, level 0)', config_filter={'algo': 'brotli', 'compression_level': 0, 'cores': 'single'}), BenchmarkEntry(benchmark_id='passmark:cpu_string_sorting_test', weight=0.05, label='PassMark string sorting')])}

Workload profile definitions keyed by workload ID.