All Tags
AWS
ai
algorithm-design
architecture
browser
cloud
cloud-efficiency
cloud-principles
cost-reduction
data-centric
data-compression
data-processing
deployment
design
documentation
edge-computing
email-sharing
energy-efficiency
energy-footprint
enterprise-optimization
green-ai
hardware
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llm
locality
machine-learning
maintainability
management
measured
microservices
migration
mobile
model-optimization
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multi-objective
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parameter-tuning
performance
queries
rebuilding
scaling
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storage-optimization
strategies
tabs
template
testing
workloads
Tactic(s) tagged with "energy-footprint"
- Select nearby regions with better renewable energy rates (AT)
- Avoid use of byte-code (SP)
- Batch I/O (SP)
- Code migration (SP)
- Compiler optimization (SP)
- Decrease algorithmic complexity (SP)
- Efficient GUI (SP)
- Free or unmap unneeded memory (SP)
- Keep 3rd party software up-to-date (SP)
- Lazy loading (SP)
- Less frequent or avoiding polling (SP)
- Put application to sleep (SP)
- Reduce data redundancy (SP)
- Reduce memory leaks (SP)
- Reduce QoS dynamically (SP)
- Reduce transparency and abstractions (SP)
- Static GUI (SP)
- Use asynchronous I/O (SP)
- Use efficient queries (SP)
- Use JIT compiler (SP)
- Use low-level programming (SP)
- Choose an energy efficient drift detection algorithm (SP)
- Static detection of flaky tests (SP)
- Mock highly consuming functions (SP)
- Use energy aware test suite prioritization (SP)
- Follow-the-sun test scheduling (SP)
- Rethink Digital Communication and Meetings (SP)
- Limit Ensemble Size (AT)
- RAG Context Caching (AT)
- RAG Context Filtering and Compression (AT)
- Use Majority Voting (AT)
- Use Subset-Based Training (AT)
- Early Stopping in Training (AT)
- Energy Efficient Hardware (AT)