Waste and under-utilization
- Annual compute spend is lost across large job volumes
- Several million CPU-hours can be wasted each year
- Average utilization sits far below requested capacity
Service Accelerator · HPC
Workflow optimization for Scientific Computing
The HPC Resource Intelligence Agent helps teams reduce compute waste, improve resource sizing, and surface actionable recommendations before jobs hit the cluster.
- Predicts CPU, memory, and runtime requirements at submission time
- Analyzes historical job behavior to recommend right-sized allocations
- Supports dashboard, CLI, and API-driven optimization workflows
Customer Use Case
Illustrative scientific computing efficiency signals from the workflow optimization accelerator.
56%
Average CPU and memory utilization across millions of jobs
34%
Over-provisioned jobs identified as recovery opportunities
35
Engineered features used during ingestion from workflow and user history
6+ hrs
Illustrative queue delays caused by inefficient allocation patterns
The business problem
When workloads are routinely over-sized, clusters lose capacity, costs rise, and scheduling performance suffers. The result is wasted compute, limited visibility, and slower execution across shared infrastructure.
Operational friction
- Inflated queue wait times slow delivery
- Teams lack visibility into where waste is happening
- Over-provisioning reduces throughput on shared clusters
HPC Resource Intelligence Agent
This ML-powered layer forecasts resource needs from historical job patterns and turns that prediction into practical recommendations teams can act on immediately.
What it does
- Predicts CPU, memory, and runtime at submission time
- Analyzes millions of historical jobs to recognize workflow patterns
- Generates right-sized recommendations with copy-ready submission guidance
- Works through dashboard, CLI, and API integration
Why it matters
- Reduces waste from over-provisioned jobs
- Improves queue efficiency and unlocks additional cluster capacity
- Creates a self-service path to immediate ROI
- Requires zero SQL for end users
How it works
A clear four-step workflow transforms operational history into smarter allocation decisions.
1
Ingest
Capture 35 engineered features from workflow and user history to establish the input context.
2
Predict
Use a multi-output regressor to estimate CPU, memory, and runtime needs before execution.
3
Recommend
Return right-sized allocations with confidence and ready-to-use job submission guidance.
4
Optimize
Make optimization continuous with self-service dashboard and CLI workflows.
Accelerator views
Real interface views show how the Workflow Optimization Engine summarizes cluster health, recommends right-sized resources, and identifies workflows and users with the highest waste.
Home page: workflow optimization statistics
The landing view summarizes total jobs, users, CPU and memory efficiency, wasted cost, model status, cluster health, and data coverage in one operating view.
Recommendation engine
Users enter job names, workflow patterns, CPU, memory, GPU, runtime, queue, project, and environment details to receive model-backed right-sizing guidance.
Top workflows with resource waste
Workflow analytics reveal high-volume, low-efficiency patterns so platform teams can focus optimization where the impact is highest.
Top users with resource waste
User activity views highlight heavy usage, efficiency variation, and coaching opportunities for targeted HPC optimization.
Business impact
Optimization is positioned as a cost-reduction strategy and a throughput multiplier for HPC operations.
Efficiency
Right-sized jobs
Recover capacity from jobs that request more compute than they actually consume.
Throughput
Reduced wait time
Improve queue behavior and fit more jobs onto the same infrastructure.
Return
Immediate ROI
Enable self-service retraining and recommendation loops that drive measurable value quickly.
Ready to reduce HPC waste and queue friction?
Book a walkthrough to see how Clovertex can apply workflow optimization, right-sizing recommendations, and continuous efficiency insights to your HPC environment.