memory-profiling-and-monitoring

Memory profiling and monitoring

Summary

Systematically measure and record peak memory usage during computationally intensive single-cell analysis operations (e.g., spectral embedding) to verify that algorithms meet claimed space complexity and scale to large datasets. This skill is essential for validating scalability claims and identifying memory bottlenecks in matrix-free and sparse-matrix workflows.

When to use

When benchmarking or validating the scalability of single-cell algorithms that claim linear or sublinear space complexity, particularly when processing datasets with ≥10 million cells. Apply this skill during spectral embedding, co-embedding, or other dimension-reduction operations on large sparse matrices in CSR format to confirm that memory usage remains manageable and does not exceed system constraints.

When NOT to use

• When profiling algorithms already known to be in-memory (e.g., dense matrix operations on small datasets <1M cells) where memory monitoring provides no new insight.
• When the algorithm's space complexity is not publicly documented or claimed; memory profiling alone cannot validate an unspecified claim.
• When the computational platform (e.g., GPU-accelerated or distributed-memory system) uses memory management schemes that are opaque to Python-level profilers.

Inputs

• single-cell count matrix with ≥10 million cells in CSR format
• algorithm implementation (e.g., tl.spectral from SnapATAC2)
• similarity metric specification (e.g., cosine similarity)
• system profiler configuration (memory_profiler or psutil)

Outputs

• peak memory usage (bytes or MB)
• wall-clock runtime for reference
• memory vs. cell count plot or table
• assessment of observed vs. claimed space complexity

How to apply

Execute the target algorithm (e.g., tl.spectral with cosine similarity metric) on your dataset while simultaneously monitoring memory consumption using a dedicated memory profiler such as memory_profiler or psutil. Record the peak memory usage throughout the entire spectral decomposition operation, noting the timestamp and algorithm phase (e.g., matrix initialization, eigendecomposition, output assembly). After execution, analyze the measured peak memory against the algorithm's documented space complexity claim by plotting execution metrics (memory vs. dataset size or cell count). Compare observed memory behavior against the claimed linear or sublinear scaling to identify whether the algorithm meets its specification or reveals unexpected memory growth.