This workshop addressed practical strategies for profiling quantum-chemical applications and scaling them on shared high-performance computing resources. Instructors demonstrated sampling-based profilers and lightweight timing hooks to identify compute-bound, memory-bound, and I/O-bound phases for Hartree–Fock, correlated ab initio, and DFT workloads. Participants learned to interpret flame graphs and roofline-style summaries that make bottlenecks visible and help prioritize optimization efforts without over-engineering.
The session detailed queue-aware job design: predictable wall-time estimates, checkpoint intervals, deterministic seeds, and idempotent post-processing. Examples compared MPI, OpenMP, and hybrid configurations, noting typical scaling envelopes and diminishing returns beyond node-local memory bandwidth or interconnect constraints. We discussed file-system behavior under contention, strategies for read-heavy integral reuse, and compression/cleanup policies that reduce storage footprint while preserving provenance.
Attendees received reproducible pipeline templates with parameterized launch scripts, small validation inputs for rapid regression tests, and guidance on documenting hardware, compiler flags, BLAS/LAPACK backends, and library versions. By treating performance engineering as part of scientific method rather than afterthought, the workshop equips researchers to run larger, clearer, and fairer comparisons—accelerating review and collaboration.