How to Use CUDA-Z to Measure GPU Performance

CUDA-Z: Quick Benchmarking for NVIDIA GPUsCUDA-Z is a lightweight, open-source tool designed to quickly gather information and run simple benchmarks on NVIDIA GPUs using the CUDA platform. It’s similar in spirit to CPU-Z but focused on CUDA-capable hardware: reporting device capabilities, memory characteristics, compute throughput estimates, and basic bandwidth/latency tests. For engineers, system builders, and developers who need a fast snapshot of GPU characteristics or a simple verification tool, CUDA-Z is a convenient starting point before diving into heavier profilers like NVIDIA Nsight or nvprof.

What CUDA-Z Measures

CUDA-Z provides several categories of output that help you understand both the hardware and how it behaves under basic workloads:

• Device information: model name, compute capability, CUDA driver and runtime versions, number of SMs (streaming multiprocessors), clock speeds (core and memory), PCI bus information.
• Memory specs and tests: total memory, memory clock, memory bus width, theoretical memory bandwidth, and measured memory bandwidth from simple copy/read/write tests.
• Compute capabilities and throughput: number of cores, peak single-precision FLOPS estimates (based on clock and core counts), and simple vector-add or matrix-like microbenchmarks to estimate practical throughput.
• Latency and transfer tests: host-to-device and device-to-host transfer bandwidths and latency for different buffer sizes, plus device-to-device transfer performance.
• GPU occupancy hints: information that helps infer occupancy (registers per block, shared memory availability), useful to estimate how many concurrent warps/threads a kernel might sustain.

Why Use CUDA-Z

• Quick diagnostics: When you want to confirm that CUDA is properly installed, verify the GPU model and driver compatibility, or check that clock speeds and memory sizes match manufacturer specs.
• Baseline benchmarking: For a rapid, portable baseline to compare different machines or to detect gross performance regressions (for example after driver updates or system changes).
• Low overhead: It’s lightweight—runs quickly, requires minimal configuration, and doesn’t demand deep knowledge of CUDA profiling tools.
• Portable and open-source: Often available for Windows and Linux; source code lets you inspect or modify tests if desired.

Installing and Running CUDA-Z

Installation is straightforward:

• On Windows: download the prebuilt executable or installer and run it. Some builds are distributed as zip archives—extract and run the exe.
• On Linux: prebuilt binaries are sometimes available; otherwise compile from source (requires CUDA toolkit and a C++ compiler). Typical steps: clone the repo, ensure CUDA toolkit and headers are found, build with make or the provided build scripts, then run the binary.

When started, CUDA-Z offers a GUI and usually a command-line mode to run specific tests headlessly. The GUI displays device summaries and allows you to select tests for memory and compute. Command-line mode is useful for scripting or collecting results on many machines.

Interpreting Results

• Device details confirm identity and compatibility. Ensure driver and runtime versions meet your application requirements.
• Compare measured memory bandwidth to the theoretical figure (memory clock × bus width × 2 for DDR, adjusted by interface). Large discrepancies can indicate thermal throttling, incorrect BIOS settings, or driver issues.
• Transfer bandwidth tests: consider both small and large buffer sizes. Small-buffer performance is dominated by latency; large buffers reflect sustained throughput. If host-device transfers are low, check PCIe link width/speed (x16 vs x8, Gen3 vs Gen4).
• Compute throughput estimates are approximations. They help spot misconfigurations (like clocks being locked low due to power/thermals) but aren’t substitutes for kernel-level profiling.
• If occupancy hints show limited resources (registers/shared memory), kernel-level tuning may be needed to improve utilization.

Example Use Cases

• System integrators validating multiple workstations after assembly to ensure GPUs are correctly seated and perform within expected ranges.
• A developer verifying that a CI machine’s GPU performance remains stable after system updates.
• A helpdesk technician gathering quick diagnostic data from a user’s machine to triage performance complaints.

Limitations

• Not a full profiler: CUDA-Z offers simple microbenchmarks and device info but won’t replace tools like NVIDIA Nsight, nvprof, or CUPTI for detailed kernel analysis, memory reuse analysis, or timeline-based tracing.
• Synthetic nature: results are useful for comparisons and basic checks but may not reflect real-world application behavior that depends on memory access patterns, kernel divergence, or complex synchronization.
• Accuracy depends on system state: background processes, thermal conditions, and power-management settings can affect measurements.

Tips for Reliable Measurements

• Run tests after allowing the GPU to reach a steady thermal state (warm-up runs).
• Disable aggressive power-saving modes if you need peak throughput measurements; be aware this changes real-world energy use.
• Compare similar-sized buffers and repeat tests multiple times to average out transient variability.
• When testing transfers, ensure the CPU and system memory are not overloaded by other tasks.

Extending CUDA-Z

Because CUDA-Z is open-source, you can:

• Add custom microbenchmarks tailored to your workload (e.g., memory access patterns resembling your application).
• Automate results collection across machines to build a fleet-wide performance database.
• Integrate CUDA-Z output into monitoring dashboards for quick trend detection.

Conclusion

CUDA-Z is a practical, low-friction tool for quickly checking CUDA-capable NVIDIA GPUs: confirming hardware details, running simple bandwidth/latency and compute microbenchmarks, and establishing baseline performance numbers. Use it as a first step in performance troubleshooting or fleet validation, then move to deeper profiling tools when you need detailed kernel-level insights.