Comparing Scimark Multigraphics Lite vs. Full SciMark Tools

Scimark Multigraphics Lite: Performance Tips and Tricks

1. Keep software up to date

• Update: Install the latest Scimark Multigraphics Lite build and GPU drivers to get bug fixes and performance improvements.
• Changelogs: Check release notes for performance-related fixes before benchmarking.

2. Use appropriate test sizes

• Right problem size: Choose benchmark/problem sizes that fit your GPU memory to avoid paging or reduced occupancy.
• Scale tests: Run small, medium, and large sizes to see where throughput and latency trade off.

3. Optimize input/output formats

• Use native formats: Prefer the file and texture formats recommended by the tool to avoid costly conversions.
• Batch I/O: Group reads/writes to minimize I/O overhead during runs.

4. Tune parallel settings

• Threads/blocks: Match kernel launch parameters (threads per block, blocks per grid) to your GPU’s architecture—use multiples of warp/wavefront size.
• Stream concurrency: Use multiple CUDA/OpenCL streams if the tool exposes them to overlap compute and data transfer.

5. Minimize data transfers

• Pinned memory: Use pinned (page-locked) host memory when transferring large buffers to reduce transfer latency.
• Keep data on device: Reuse device-resident data across runs rather than copying repeatedly.

6. Profile and identify hotspots

• Profilers: Use NVIDIA Nsight / AMD Radeon GPU Profiler / Intel VTune as appropriate to find bottlenecks.
• Focus: Prioritize optimizing kernels that consume the most time or memory bandwidth.

7. Improve memory access patterns

• Coalesced access: Arrange data so threads access contiguous memory to maximize throughput.
• Shared/local memory: Use shared (CUDA) or local (OpenCL) memory for frequently reused data to reduce global memory pressure.

8. Reduce branching and divergent paths

• Branch avoidance: Refactor kernels to minimize divergent branches within warps/wavefronts.
• Predication: Where appropriate, use predicated operations instead of branching.

9. Leverage hardware features

• Tensor/RT cores: If available and supported by the benchmark kernels, enable specialized units for matrix ops.
• FP16/BF16: Consider lower-precision compute modes where acceptable to increase throughput and decrease memory use.

10. Repeatable, controlled runs

• Environment: Disable background apps, set consistent power/performance modes, and ensure thermal stability.
• Multiple runs: Average several runs and report variance to avoid misleading single-run results.

11. Compare fairly

• Baseline: Keep a documented baseline configuration (driver, OS, power settings).
• Normalized metrics: Report per-watt and per-core or per-GFLOP metrics when comparing systems.

12. Common quick fixes

• Power state: Set GPU to performance mode to prevent throttling.
• Cooling: Improve cooling to avoid thermal throttling during extended runs.
• Dependencies: Use optimized math libraries (cuBLAS, MKL, etc.) if the benchmark can link to them.

If you want, I can generate a short checklist tailored to a specific GPU/OS or create command-line examples for profiling and launching tuned runs.