Automatic Workload Generation

Concept

Automatic Workload Generation is a methodology for systematically synthesizing representative training applications that cover a broad range of program behavior state-space, primarily used to train machine learning models for hardware performance and power prediction. By exposing low-level program characteristics as user-controllable knobs, it overcomes the limitations of standard benchmark suites, achieving over 11x higher state-space coverage than suites such as SPEC CPU2006, MiBench, MediaBench, and TPC-H, and improving the accuracy of ML-based prediction systems by 2.5x to 3.6x.

First seen 6/13/2026

Last seen 6/13/2026

Evidence 5 chunks

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Automatic Workload Generation

Overview

Automatic Workload Generation is a methodology and framework for systematically generating synthetic training applications whose low-level program characteristics can be precisely controlled. It is designed to produce training sets that cover a wide region of the program behavior state-space, enabling the construction of more accurate and generalizable machine learning (ML) models for tasks such as performance and power prediction. The approach was introduced to overcome the limitations of relying on hand-picked standard benchmark suites, which were shown to leave large portions of the application state-space uncovered and to require tedious manual effort to extend.

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RELATIONSHIPS

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Genesys ← implements 100% 3e

Genesys implements the concept of automatic workload generation for creating representative training sets.

Predictive Benchmarking part of → 90% 2e

Automatic workload generation is a key component of predictive benchmarking methodology.

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CITATIONS

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[1] Automatic Workload Generation is a workload generation framework that enables systematic generation of representative training set applications, providing a wider coverage of program behavior state-space for effectively training machine learning models. Genesys: Automatically Generating Representative [Workloads] (SAMOS 2016)

[2] The methodology allows control of a set of key workload-specific characteristics using easy-to-use, programmable knobs, providing the ability to generate applications targeting specific program properties. Genesys: Automatically Generating Representative [Workloads] (SAMOS 2016)

[3] SpreadRatio is a novel metric defined to compare state-space coverage, based on the area of the convex hull envelope surrounding the program points. Genesys: Automatically Generating Representative [Workloads] (SAMOS 2016)

[4] Automatically generated training sets achieve over 11 times higher state-space coverage than popular standard benchmarks such as SPEC CPU2006, MiBench, MediaBench, and TPC-H. Genesys: Automatically Generating Representative [Workloads] (SAMOS 2016)

[5] Modeling using targeted synthetic training sets improves the predictability/accuracy of two machine-learning-based power and performance prediction systems by over 2.5x and 3.6x respectively. Genesys: Automatically Generating Representative [Workloads] (SAMOS 2016)

[6] Genesys is built upon a set of core workload-specific metrics (instruction-level, control-flow, and memory-access) that can be controlled systematically; the user fixes some values and the framework randomizes the rest within reasonable bounds to achieve well-rounded state-space coverage. Genesys: Automatically Generating Representative [Workloads] (SAMOS 2016)

[7] Core metrics include: Instruction mix (5 categories), Instruction count, Instruction cache miss rate (ICMR), Instruction-level parallelism (ILP, 32 bins), Average basic block size, Branch transition rate (BTR), Branch misprediction rate, Data footprint, Regular/irregular behavior, Spatial locality stride bins (32 bins), Temporal locality bins (8 bins), L1/L2 Data cache miss rates. Genesys: Automatically Generating Representative [Workloads] (SAMOS 2016)

[8] The user provides target metric values that form a Workload-Specific Profile, which is fed into a code generator that produces a suite of synthetic programs (Syn Prog 1, Syn Prog 2, ...). Genesys: Automatically Generating Representative [Workloads] (SAMOS 2016)

[9] ML-based modeling relies heavily on training sets; an ill-formed training set can affect the statistical model (e.g., cause overfitting), while a well-formed training set improves accuracy and applicability. Genesys: Automatically Generating Representative [Workloads] (SAMOS 2016)

[10] Hand-tuned micro-benchmarks are tedious to create, time-consuming, and produce too few program instances for statistical training; standard benchmark suites such as SPEC CPU2006 have been shown to exhibit similar low-level behavior across application domains, leaving much of the state-space uncovered. Genesys: Automatically Generating Representative [Workloads] (SAMOS 2016)