Gpu acceleration cuda configuration

GPU Acceleration via CUDA Configuration

Summary

Configure and deploy CUDA-accelerated GPU computing to enable sub-15-minute clustering of large-scale mass spectrometry datasets (e.g., 25 million spectra) using hyperdimensional encoding and Hamming distance computation on NVIDIA GPUs. This skill trades GPU memory requirements for dramatic runtime reduction in compute-intensive proteomics workflows.

When to use

When clustering or encoding large MS/MS spectra datasets (>1 million spectra) where CPU-only runtime exceeds practical thresholds (hours to days). Specifically applicable when hyperdimensional computing or distance-matrix operations dominate the computational load and NVIDIA GPU hardware (GTX 1080Ti or RTX 3090) is available with sufficient VRAM.

When NOT to use

• Input dataset is <100k spectra: CPU clustering may be faster due to GPU initialization overhead.
• GPU with <6GB VRAM and dataset >10 million spectra: memory pressure will force excessive batch-size reduction or fail.
• Non-NVIDIA GPU or missing CUDA toolkit: HyperSpec requires CUDA-capable hardware and has been tested only on GTX 1080Ti and RTX 3090.

Inputs

• MGF (mass spectrometry peak) files containing raw MS/MS spectra
• Dataset path containing one or more MGF files
• Spectra metadata (precursor m/z, charge state, retention time, scan number)

Outputs

• Parquet file with clustered spectra metadata (bucket, precursor_charge, precursor_mz, identifier, scan, retention_time, cluster, is_representative)
• CSV file with cluster assignments (one spectrum and cluster label per line)
• Checkpoint file containing encoded hypervectors (optional, for resumption)

How to apply

Install Python 3.8+ with CUDA environment and configure the GPU cluster task using the --use_gpu_cluster flag. Set batch size (default 5000 spectra per batch) to balance GPU memory usage against throughput; for large datasets like PXD000561 (25M spectra), use GTX 3090 or higher-memory GPU. Enable GPU-optimized Hamming distance kernels and HD encoding modules during spectra preprocessing. Verify CUDA device availability and monitor memory utilization during batch processing to prevent out-of-memory failures. The HD encoding module runs on GPU with batch_size parameter to trade memory for speed; lower batch_size if GPU memory is exhausted, higher batch_size if memory permits and latency is critical.

Related tools

• HyperSpec (GPU-accelerated spectra clustering framework with CUDA kernels for HD encoding and Hamming distance computation) — https://github.com/wh-xu/Hyper-Spec
• CUDA (GPU compute platform required for parallel HD encoding, Hamming distance matrix computation, and optional DBSCAN clustering on GPU)
• Python 3.8+ (Runtime environment and interface for HyperSpec command-line invocation and batch parameter configuration)

Examples

python src/main.py ~/dataset/ ./output.csv --cpu_core_preprocess=4 --cluster_alg dbscan --use_gpu_cluster --cluster_charges 2 3 --eps=0.2 --batch_size=5000 --refine

Evaluation signals

• End-to-end runtime on 25M-spectra dataset is <15 minutes on GTX 3090 (vs. hours on CPU baseline).
• GPU memory usage (monitored via nvidia-smi) remains below device capacity throughout batch processing; no OOM errors occur.
• Output parquet/CSV file contains cluster IDs for all input spectra with no missing or null cluster assignments.
• Hamming distance matrices computed on GPU yield identical clustering results (cluster labels, representative spectra) compared to CPU reference implementation.
• Batch processing completes without CUDA synchronization errors or kernel launch failures.

Limitations

• Clustering PXD000561 (25M spectra) requires GTX 3090 with large VRAM; smaller GPUs (e.g., GTX 1080Ti) support smaller datasets only.
• Performance gains are specific to hyperdimensional encoding and Hamming distance; standard DBSCAN on CPU may not show proportional speedup.
• CUDA environment setup and GPU driver installation are prerequisites; not portable to CPU-only or non-NVIDIA platforms.
• Batch size tuning is dataset- and hardware-dependent; no automatic heuristic provided for optimal batch_size selection.

Evidence

• [intro] HyperSpec shortens the runtime on the draft human proteome dataset with 25 million spectra from a few hours to <15 minutes.: "HyperSpec shortens the runtime on the draft human proteome dataset with 25 million spectra from a few hours to <15 minutes."
• [readme] HyperSpec requires Python 3.8+ with CUDA environment. A GPU should be installed properly.: "HyperSpec requires Python 3.8+ with CUDA environment. A GPU should be installed properly."
• [readme] Clustering for PXD000561 dataset requires GTX 3090 with larger memory: "Clustering for PXD000561 dataset requires GTX 3090 with larger memory"
• [readme] The encoding module is implemented and optimized for GPU for shorter runtime.: "The encoding module is implemented and optimized for GPU for shorter runtime."
• [readme] HyperSpec implements very efficient Hamming distance computation kernels on GPU.: "HyperSpec implements very efficient Hamming distance computation kernels on GPU."
• [readme] --batch_size The batch size for HD encoding on GPU. (default: 5000): "--batch_size The batch size for HD encoding on GPU. (default: 5000)"
• [readme] --use_gpu_cluster Flag that determines whether to use DBSCAN on GPU. (default: True): "--use_gpu_cluster Flag that determines whether to use DBSCAN on GPU. (default: True)"