Experiments on Joulie

CPU-Only Benchmark

Mon, 01 Jan 0001 00:00:00 +0000

This page reports results from the CPU-only cluster benchmark experiment (KWOK, 40 nodes):

experiments/01-cpu-only-benchmark/

Scope

The benchmark compares three baselines on a CPU-only cluster with 40 KWOK nodes across 3 hardware families, running on a real Kind+KWOK Kubernetes cluster:

A: Simulator only (no power management)
B: Joulie with static partition policy
C: Joulie with queue-aware dynamic policy

The experiment demonstrates energy savings achievable through CPU RAPL capping alone, without GPU complexity.

Heterogeneous GPU Cluster Benchmark

Mon, 01 Jan 0001 00:00:00 +0000

This page reports results from the heterogeneous GPU cluster benchmark experiment (KWOK, 41 nodes):

experiments/02-heterogeneous-benchmark/

Scope

The benchmark compares three baselines on a heterogeneous GPU cluster mixing 5 distinct GPU hardware families plus CPU-only nodes, running on a real Kind+KWOK Kubernetes cluster:

A: Simulator only (no power management)
B: Joulie with static partition policy
C: Joulie with queue-aware dynamic policy

The experiment demonstrates energy savings achievable through combined CPU and GPU RAPL capping on a mixed-vendor GPU fleet.

Homogeneous H100 NVL Benchmark

Mon, 01 Jan 0001 00:00:00 +0000

This page reports results from the homogeneous H100 NVL cluster benchmark experiment (KWOK, 41 nodes):

experiments/03-homogeneous-h100-benchmark/

Scope

The benchmark compares three baselines on a homogeneous GPU cluster with 33 identical NVIDIA H100 NVL nodes plus 8 CPU-only nodes, running on a real Kind+KWOK Kubernetes cluster:

A: Simulator only (no power management)
B: Joulie with static partition policy
C: Joulie with queue-aware dynamic policy

Hypothesis

Joulie performs better on a homogeneous cluster because every GPU node can accept any GPU job, eliminating the vendor/product-specific placement constraints that restrict policy flexibility in the heterogeneous case.

Scoring Formula Validation

Mon, 01 Jan 0001 00:00:00 +0000

This page reports results from the energy-aware scheduling formula validation experiment:

experiments/04-scoring-formula-validation/

Objective

Validate Joulie’s energy-aware scheduling formula by demonstrating that power-aware scheduling improves energy efficiency compared to standard Kubernetes bin-packing (MostAllocated), using a Modelica FMU (DXCooled Airside Economizer) for physically-accurate cooling/PUE computation.

Two scales tested:

Small cluster (28 nodes) — formula tuning and component selection
Large cluster (2,500 nodes) — production-scale validation with H100 GPUs

The experiment also validated the evolution from a legacy multi-component formula to the current streamlined Joulie scoring formula.