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LSPERF06-BP01 Run comprehensive, benchmark-driven assessments - Life Sciences Lens
Implementation guidance

LSPERF06-BP01 Run comprehensive, benchmark-driven assessments

Execute standardized application-specific benchmarks using representative datasets across multiple hardware solutions (GPUs, FPGAs, ASICs), measuring not only raw performance but also performance-per-watt, scaling efficiency, and cost-effectiveness with your actual production workloads in molecular dynamics (GROMACS, NAMD, AMBER) and genomics (BWA-MEM, GATK, Minimap2).

Desired outcome: Conduct comprehensive benchmarks of scientific applications across diverse hardware solutions to measure performance, energy efficiency, scaling, and cost-effectiveness for optimal computational research infrastructure selection.

Level of risk exposed if this best practice is not established: High

Implementation guidance

Establish Standardized Testing Methodology:Develop rigorously controlled benchmark protocols specific to life sciences applications. Standardized methodologies facilitate consistent, reproducible results that can be compared across different hardware architectures and over time.

Evaluate Comprehensive Performance Metrics:Measure multiple dimensions of hardware performance beyond raw speed. Comprehensive metrics provide a holistic view of hardware value, including energy efficiency, cost factors, and scaling characteristics.

Compare Across Hardware Accelerator Types:Conduct benchmarks across diverse specialized computing architectures. Systematic comparison across acceleration technologies reveals which hardware best matches specific application characteristics.

Validate with Production Workloads:Use actual production datasets and workflows for benchmark validation. Real-world validations make sure benchmark results translate to meaningful improvements in day-to-day research operations.

Create Decision Support Framework: Develop a structured approach to translate benchmark data into procurement decisions. A formal framework validates objective evaluation of hardware options based on quantified performance metrics aligned with research priorities.

Implementation steps

  1. Define benchmark frameworks with standardized tests for molecular dynamics and genomics.

  2. Implement measurements for performance-per-watt metrics and total cost calculations.

  3. Execute testing across GPU, FPGA, and ASIC solutions for bioinformatics.

  4. Perform validation using actual simulations to verify benchmark predictions.

  5. Establish hardware selection using scoring matrices and ROI models.