Synthetic Workload

A synthetic workload is defined by a statistical distribution of request parameters, not fixed values. This allows engineers to model real-world unpredictability in a repeatable way. Key parameters include:

• Request arrival rate: Often modeled as a Poisson process to simulate random user interactions.
• Input prompt length: Typically follows a log-normal or similar distribution based on production data analysis.
• Output token count: Can be fixed, sampled from a distribution, or dynamically determined by a target model's behavior.
• Request concurrency: The number of simultaneous requests is precisely controlled to test system limits. This controlled variability enables the creation of stress tests, load tests, and soak tests that systematically explore a system's performance envelope.

The primary engineering value of a synthetic workload is its perfect reproducibility. Unlike unpredictable real-world traffic, a synthetic workload can be replayed identically. This is achieved through:

• Seeded random number generators: All stochastic elements (arrival times, prompt lengths) are driven by a fixed seed.
• Pre-generated request traces: A sequence of requests can be saved and re-submitted verbatim.
• Parameterized templates: Input prompts are generated from templates with variable slots filled deterministically. This reproducibility is critical for A/B testing infrastructure changes, identifying performance regressions, and establishing a performance baseline. It allows engineers to assert that a 10% latency improvement is due to a kernel optimization, not a fluctuation in traffic pattern.

Synthetic workloads are engineered to apply specific, exaggerated load patterns to probe system weaknesses that may not be visible under normal traffic. Common targeted patterns include:

• Burstable traffic: Sudden, massive spikes in QPS (Queries Per Second) to test autoscaling and cold start latency.
• Long-running sequences: Requests that generate thousands of output tokens to stress KV cache management and memory persistence.
• Mixed precision workloads: Interleaving FP16, BF16, and INT8 requests to test mixed precision inference pipelines and dynamic casting.
• Adversarial scheduling: Deliberately submitting batches of requests with maximally varying input lengths to challenge continuous batching schedulers. These patterns help answer "what-if" scenarios and validate Service Level Objectives (SLOs) under extreme but plausible conditions.

A key design goal is to isolate and measure the performance of specific subsystems. Synthetic workloads achieve this by holding all but one variable constant. Examples include:

• CPU-bound profiling: Sending very short prompts to minimize GPU compute and highlight pre/post-processing CPU overhead.
• Memory bandwidth tests: Using requests that trigger large context windows to fill the KV cache, testing memory-bound performance and cache eviction policies.
• Network latency injection: Adding artificial delay to request submission to decouple network effects from pure inference engine performance.
• Compute kernel analysis: Crafting inputs that cause specific neural network operators (e.g., attention, feed-forward layers) to dominate execution time for performance profiler analysis. This isolation is fundamental to bottleneck analysis and applying the roofline model to understand hardware utilization limits.

High-fidelity synthetic workloads generate semantically valid inputs, not random token strings. Validity is crucial because model performance (latency, memory access patterns) can depend on input meaning and structure. Techniques include:

• Template-based generation: Using grammatically correct sentence templates (e.g., "Summarize the following text: {variable_text}").
• Retrieval-augmented synthesis: Sampling coherent text snippets from a reference corpus to ensure natural language statistics.
• Task-specific construction: For a translation benchmark, using valid source/target language pairs; for a code model, using syntactically correct code snippets.
• Adversarial example inclusion: Intentionally adding ambiguous or complex phrasing to test worst-case reasoning paths. This ensures the workload exercises the model's full computational graph realistically, affecting metrics like Time per Output Token (TPOT).

Effective synthetic workloads are not created in a vacuum; they are statistically calibrated using telemetry from production systems (real-world workloads). This calibration involves:

• Distribution fitting: Analyzing production logs to fit probability distributions for request inter-arrival times, input lengths, and popular query types.
• Topical modeling: Ensuring the mix of questions (e.g., coding, creative writing, analysis) matches the production distribution.
• Time-of-day patterns: Simulating diurnal cycles or usage spikes typical of the application's user base.
• Failure mode injection: Mimicking the rate and type of malformed requests seen in production (e.g., empty prompts, extremely long contexts) to test system resilience. The result is a proxy workload that is both reproducible and representative, enabling accurate forecasting of production performance from staging environment tests.

Synthetic workloads enable the creation of a reproducible performance baseline under controlled conditions. By generating requests with specific, known characteristics (e.g., fixed input/output token lengths, deterministic patterns), engineers can isolate system performance from the noise of unpredictable real-world traffic. This is essential for A/B testing infrastructure changes, such as evaluating a new GPU driver, a different model quantization scheme, or an updated inference server version. Benchmarks like MLPerf Inference rely on precisely defined synthetic workloads for fair comparison across systems.

Before deploying a model to production, engineering teams must answer critical questions: How many GPUs are needed? When will the system saturate? Synthetic workloads allow for predictive scaling analysis by simulating future traffic loads. Engineers can generate workloads that mimic projected user growth (e.g., 2x, 10x current traffic) to identify bottlenecks in memory, compute, or network I/O. This process involves constructing throughput-latency curves to find the saturation point and determine the required resources to meet Service Level Objectives (SLOs) for both median and tail latency (P99).

The goal is to intentionally break the system in a test environment to prevent failure in production. Synthetic workloads are engineered to push systems beyond normal operating limits to uncover:

• Resource contention under extreme concurrency.
• Memory-bound vs. compute-bound behavior cliffs.
• Cold start latency impact during rapid autoscaling events.
• Failure modes of KV cache management and attention mechanisms with very long contexts. By simulating worst-case scenarios—such as sudden traffic spikes or sequences of maximum-length prompts—teams can validate system resilience and define safe operating envelopes.

Synthetic workloads are a scalpel for performance debugging. By crafting workloads that stress specific subsystems, engineers can perform targeted bottleneck analysis. Examples include:

• Creating a workload of many short requests to test the efficiency of continuous batching schedulers.
• Generating requests with highly variable output lengths to assess the impact on Time per Output Token (TPOT).
• Designing input patterns that cause specific neural network operators or memory access patterns to become hotspots, visible in a performance profiler. This controlled approach isolates variables, making it possible to measure the precise impact of optimizations like operator fusion or mixed precision inference.

Synthetic workloads are integral to a CI/CD pipeline for machine learning systems. They provide a continuous integration test suite for performance. Before any code or configuration change is deployed, a suite of synthetic workloads can be run to automatically detect performance regressions. This ensures that updates to the model, inference engine, or underlying infrastructure do not violate predefined Service Level Indicators (SLIs), such as P99 latency staying below 200ms. This automated guardrail is crucial for maintaining steady-state performance and user trust.

When selecting between different inference servers, hardware accelerators, or cloud instances, synthetic workloads provide an objective, apples-to-apples comparison framework. Teams can design a workload that mirrors their expected production mix of request types and use it to evaluate:

• Throughput (QPS/TPS) and latency trade-offs across different platforms.
• Cost-efficiency (inferences per dollar) under realistic load patterns.
• The effectiveness of vendor-specific optimizations for scenarios like mixture of experts inference or speculative decoding. This data-driven approach moves selection criteria beyond peak theoretical FLOPs to measurable performance on relevant workloads.

The practice of simulating expected or peak user traffic on an inference system to measure its performance characteristics under controlled conditions. A synthetic workload is the primary input for a load test.

• Purpose: Validate system capacity and stability before production deployment.
• Method: Uses a synthetic workload generator to create request patterns mimicking real users.
• Output: Metrics for latency, throughput, and resource utilization at various load levels.

A set of established performance metrics and measurements that serve as a reference point for comparing future system changes. It is created by running standardized synthetic workloads.

• Establishment: Record P99 latency, tokens per second (TPS), and GPU utilization under a defined synthetic load.
• Use Case: Essential for detecting performance regression after software updates or configuration changes.
• Reproducibility: Requires the exact same synthetic workload and hardware environment for valid comparison.

The actual stream of production inference requests generated by end-users, representing genuine usage patterns and data distributions. This contrasts with a synthetic workload's controlled simulation.

• Characteristics: Unpredictable request arrival times, diverse prompt complexities, and non-stationary patterns.
• Limitation for Testing: Difficult to reproduce consistently for benchmarking.
• Relationship to Synthetic: A high-quality synthetic workload is designed to emulate the statistical properties (e.g., request size distribution, concurrency) of the real-world workload.

A fundamental graph that shows the relationship between a system's achieved throughput and the resulting request latency as the offered load increases. Synthetic workloads are used to trace this curve.

• X-Axis: Offered load (e.g., Queries Per Second).
• Y-Axis: Measured latency (e.g., average or P99).
• Key Regions: Linear region (latency stable), knee of the curve (latency begins to rise), and saturation point (throughput plateaus, latency spikes).
• Engineering Use: Determines the optimal operating point for balancing efficiency and responsiveness.

Refers to the high-percentile latencies in a request distribution, representing the slowest requests. Synthetic workloads are crucial for stress-testing and understanding tail latency behavior.

• Measurement: The 99th percentile (P99) latency indicates that 99% of requests were faster than this value.
• Importance: Often determines user-perceived performance; a high P99 indicates inconsistency.
• Analysis: Synthetic workloads help isolate causes of tail latency, such as resource contention, garbage collection pauses, or uneven request scheduling.

An industry-standard, peer-reviewed benchmark suite for measuring the performance of inference systems. It uses rigorously defined synthetic workloads and models to ensure fair comparisons.

• Components: Standardized models (e.g., BERT, ResNet), datasets, and reference synthetic workloads defining query samples and load patterns.
• Scenarios: Benchmarks for data center, edge, and mobile deployment contexts.
• Role of Synthetic Workloads: Provides the exact, reproducible stream of inference tasks that all systems must process, enabling objective performance ranking.