GPU Benchmarking Glossary

A numerical value representing the overall performance of hardware in a standardized test. CZNull calculates scores based on frame rates, render times, and computational throughput.

The minimum expected performance level for a given hardware configuration. Used as a reference point to compare actual test results against expected outcomes.

A decrease in benchmark scores over time, often caused by driver updates, thermal throttling, or hardware degradation. Regular CZNull testing helps identify these issues.

Artificial test scenarios designed to measure specific hardware capabilities in isolation. CZNull uses synthetic workloads to evaluate GPU compute, rendering, and memory performance.

Frames Per Second - the number of complete images rendered and displayed each second. Higher FPS indicates smoother graphics performance. CZNull measures FPS under various computational loads.

The duration required to render a single frame, measured in milliseconds. Consistent frame times (low variance) are crucial for smooth visual experience. CZNull tracks both average and 1% low frame times.

The amount of work completed per unit of time. In GPU testing, this refers to operations per second, pixels rendered, or data processed. Higher throughput indicates better parallel processing capability.

The average frame rate of the bottom 1% of frame time measurements. This metric reveals worst-case performance scenarios and stuttering issues that average FPS might hide.

The rate at which data can be transferred to and from GPU memory, measured in GB/s. Critical for texture-heavy workloads and high-resolution rendering. CZNull tests bandwidth through intensive memory operations.

Web Graphics Library - a JavaScript API for rendering interactive 2D and 3D graphics in web browsers without plugins. CZNull benchmarks are built entirely on WebGL 2.0 for cross-platform compatibility.

A program running on the GPU that controls how graphics are rendered. Includes vertex shaders (geometry processing) and fragment shaders (pixel coloring). CZNull tests shader performance through complex computational tasks.

A specialized shader program designed for general-purpose GPU computing rather than graphics rendering. Used for physics simulations, data processing, and parallel computations in CZNull's advanced benchmarks.

A command instructing the GPU to render a set of geometry. Excessive draw calls can bottleneck performance. CZNull's instanced rendering tests evaluate how efficiently your GPU handles numerous draw calls.

The process of reading color values from texture images during rendering. Complex filtering and high-resolution textures increase sampling overhead. CZNull measures texture sampling performance in memory-intensive scenarios.

The transformation and manipulation of 3D vertex data before rasterization. Includes position calculations, lighting, and coordinate transformations. Heavy vertex processing can become a performance bottleneck.

The parallel processing units in NVIDIA (CUDA cores) and AMD (Stream Processors) GPUs. More cores generally enable better parallel workload performance. CZNull's compute tests leverage these cores.

Dedicated memory on the graphics card used for storing textures, frame buffers, and computational data. Insufficient VRAM causes performance degradation. CZNull tests include memory-intensive scenarios to evaluate VRAM capacity.

Automatic reduction of GPU clock speeds when temperature exceeds safe thresholds. Prevents hardware damage but decreases performance. Extended CZNull stress tests can reveal throttling behavior.

The frequency at which GPU cores operate, measured in MHz or GHz. Higher clocks generally mean faster processing, but efficiency varies by architecture. CZNull measures effective performance rather than raw clock speeds.

The process of converting vector graphics (vertices and polygons) into raster images (pixels). A fundamental GPU operation tested in CZNull's rendering benchmarks.

A benchmark that pushes hardware to maximum capacity to evaluate stability and thermal behavior. CZNull's GPU stress tests run intensive workloads to identify system limits.

Creating realistic usage scenarios to test performance under practical conditions. CZNull simulates various graphics and compute tasks to provide comprehensive performance insights.

Evaluating performance across different operating systems and hardware configurations. CZNull's web-based approach enables consistent testing on Windows, macOS, Linux, and mobile devices.

The consistency of benchmark results across multiple test runs. High reproducibility indicates reliable measurements. CZNull minimizes variance through controlled test conditions.

Automatically adjusting test difficulty based on detected hardware capabilities. CZNull uses adaptive workloads to provide meaningful results for both low-end and high-end systems.

A rendering technique simulating realistic light behavior by tracing light ray paths. Hardware-accelerated ray tracing is available in modern GPUs. CZNull includes ray tracing workloads for supported hardware.

Drawing multiple copies of the same geometry with a single draw call, dramatically reducing CPU overhead. CZNull's instanced rendering test evaluates efficiency with massive object counts.

Executing multiple operations simultaneously using GPU's massively parallel architecture. Essential for compute-heavy tasks. CZNull's compute shader tests measure parallel processing efficiency.

Reducing texture memory footprint through specialized compression formats (DXT, ETC, ASTC). Improves memory efficiency and bandwidth utilization without significant quality loss.

Skipping rendering of objects hidden behind other geometry to save processing power. Advanced technique for optimizing complex scenes.

A collection of small sprites or objects used to simulate phenomena like fire, smoke, or explosions. Tests GPU capacity for handling numerous independent entities. CZNull includes particle system benchmarks.