Bibliographic record
Abstract
Power optimization and power control are challenging issues for server computer systems.A system can be represented as a set of components whose cooperative interaction produces useful work.These components may be heterogeneous in nature and may vary in the power consumption and power control mechanisms.Server system components may coordinate power control actions using embedded controllers or special hardware.System development tends to be a complex process that competes for performance in the presence of design constraints.These constraints may be on manufacturing cost, validation cost, area, form-factor, or operational costs.Operational cost is related to the cost of operating a system for a unit of work.Operational cost reduction requires observability, controllability, and adaptability.These features come at a price that may increase the manufacturing, design, and validation costs.Energy efficient design helps in realizing a system that minimizes power and thermal dissipation for a given performance constraints.These systems can perform one or many functions related to power/thermal management for a given performance policy: 1. Parameter tuning to reduce energy consumption for a given performance policy.This may require collective (or coordinated) tuning of system components for minimum power usage at given performance levels.2. Limiting the power of an individual component (or set of components) in a power constrained system.Power is allocated (or de-allocated) in a manner such that performance degradation is minimized to the extent possible.3. Power prediction and forecasting to avoid sudden state changes.This prediction can be at the component level or at the system level.For example, we may predict the inactivity periods between bursts of memory traffic, which allows us to proactively prepare the system for an appropriate sleep state.This avoids reactive latencies and hence increases performance.4. Distributing the available power to system components in a manner that maximizes the overall performance.One strategy may involve individual allocation (or de-allocation) due to each component's share in performance gain. 5. Using activity vectors to perform thermally balanced computing, thus avoiding hot spots.Activity data can also be used to co-schedule tasks in a contention-free and energy-efficient manner. Control Theoretic Approach to PlatformOptimization using HMM 14 www.intechopen.comFurthermore, energy-efficient systems design involves complex choices due to a variety of degrees of freedom for power parameter tuning.The process involves modeling methodology, implementation choices, and dynamic tuning.Modeling methodology includes the choice of algorithms or heuristics that tunes the state transition.Implementation choices involve the hosting of executable code in a manner such that it can access the appropriate telemetry data in an efficient manner at runtime.Additionally, it should have enough computation power to perform policy-related functions while being non-intrusive during sleep states.In recent years energy-efficient design in servers has received much primarily due• The need to reduce heat dissipation, thereby reducing the cooling costs• The need to reduce energy consumption, thereby reducing the energy-related operating costs• Strict current limits in a power-limited server rack.It may therefore be desired to maximize the rack consumption while keeping the energy limits within regulations• Capacity planning that requires efficient use of existing real-estate, which necessitates the optimal use of available racks.In general, energy efficient design helps in realizing a system that minimizes power and thermal dissipation for a given performance constraints.These systems can perform one or many functions related to power/thermal management for a given performance policy:• Parameter tuning to reduce energy consumption for a given performance policy.This may require collective (or coordinated) tuning of system components for minimum power usage at given performance levels.• Limiting the power of an individual component (or set of components) in a power constrained system.Power is allocated (or de-allocated) in a manner such that performance degradation is minimized to the extent possible.• Power prediction and forecasting to avoid sudden state changes.This prediction can be at the component level or at the system level.For example, we may predict the inactivity periods between bursts of memory traffic, which allows us to proactively prepare the system for an appropriate sleep state.This avoids reactive latencies and hence increases performance.• Distributing the available power to system components in a manner that maximizes the overall performance.One strategy may involve individual allocation (or de-allocation) due to each component's share in performance gain.• Using activity vectors to perform thermally balanced computing, thus avoiding hot spots.Activity data can also be used to co-schedule tasks in a contention-free and energy-efficient manner.• Profiling task characteristics related to (a) Task priority (b) Energy and Thermal profile (c) Optimization methodology regarding latency targets proportional to task priority. HMM approachWe face several challenges in the establishment of power/thermal monitoring infrastructure that can uncover complex deviance from an established norm.The correlation of sensors is typically separated by a significant amount of time that makes it difficult to model.In such cases, the Hidden Markov Model (HMM) is particularly useful because it can exploit 292Hidden Markov Models, Theory and Applications www.intechopen.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.032 | 0.014 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".