Energy Code

Optimizing software to cut power use and costs in data centers

REDUCING DATA CENTERS COSTS

OPTIMIZING SOFTWARE CUT POWER USE

IT Services Portfolio

Energy-to-solution profiling & optimization

Detailed measurement of energy consumption per simulation (E/S metric) using hardware counters (RAPL, NVIDIA Nsight, AMD rocprof) to identify and prioritize energy-dominant code regions without sacrificing scientific accuracy

Power-aware algorithmic redesign

Refactoring of compute-intensive kernels and data layouts to maximize flops/watt: loop fusion, memory access pattern optimization, reduced precision where acceptable, and dynamic voltage/frequency scaling guidance

Roofline + power roofline analysis

Combined performance and energy roofline modeling to reveal whether the bottleneck is compute-bound, memory-bound, or power-bound, guiding targeted optimizations that deliver the best energy efficiency gains

Green GPU/accelerator tuning

Advanced GPU kernel optimization (occupancy vs. power trade-off, asynchronous execution, reduced register pressure, warp-level efficiency) to lower total energy use on NVIDIA, AMD Instinct, and Intel Data Center GPUs

Hybrid CPU–GPU energy balancing

Workload partitioning strategies that dynamically assign tasks between CPU and GPU to minimize overall system energy consumption while meeting performance deadlines (using OpenMP target, SYCL, or oneAPI runtime controls)

Long-term energy savings roadmap

Quantified multi-year energy reduction plan with projected kWh savings, CO₂ footprint reduction, and electricity cost avoidance, including hardware upgrade recommendations and code portability for future low-power architectures