Global History Register

Definition

The Global History Register is a data structure used in branch prediction that records the outcome history of previous branches to help predict future branch behavior. This register captures the global history of taken and not taken branches, enabling more accurate predictions by leveraging past patterns to anticipate future decisions in program execution.

5 Must Know Facts For Your Next Test

1. The Global History Register typically has a fixed size, which limits how much historical data can be stored, often leading to trade-offs between accuracy and resource usage.
2. In modern processors, the Global History Register can be combined with other predictors, like local history tables, to enhance overall prediction performance.
3. Branch predictors using a Global History Register are particularly effective in workloads with repeating branch patterns due to their ability to leverage past outcomes.
4. The effectiveness of a Global History Register depends heavily on its implementation and the workload characteristics; some workloads benefit significantly while others may not.
5. As program execution evolves, the Global History Register's information must be continuously updated to maintain prediction accuracy and avoid mispredictions.

Review Questions

• How does the Global History Register improve the accuracy of branch predictions in modern processors?
  • The Global History Register improves accuracy by keeping track of the outcomes of previous branches globally. By analyzing patterns from past executions, it enables the predictor to make educated guesses about future branches. This allows processors to anticipate whether a branch will be taken or not based on historical behavior, reducing stalls in instruction pipelines and enhancing overall performance.
• Compare the role of the Global History Register with local history mechanisms in branch prediction strategies.
  • The Global History Register captures a broad view of past branch outcomes, while local history mechanisms focus on specific branches and their immediate behavior. By combining both strategies, modern predictors can achieve higher accuracy because they can utilize both global patterns and specific branch histories. This dual approach allows processors to adapt more effectively to varying execution patterns and workload characteristics.
• Evaluate the impact of the size and implementation of the Global History Register on its predictive capability and processor performance.
  • The size and implementation of the Global History Register significantly influence its predictive capability. A larger register can store more historical data, potentially leading to improved accuracy in branch predictions. However, this increased size comes at a cost of additional resources and power consumption. Balancing these factors is crucial; an optimally sized and implemented register can lead to substantial performance gains in processors, while an oversized or poorly implemented one might result in diminishing returns or even negative impacts on efficiency.

Related terms

Branch Prediction: A technique used in computer architecture to guess the direction of branch instructions to improve flow in instruction pipelines.

Pattern History Table (PHT): A table used in dynamic branch prediction that stores the history of previous branch outcomes, allowing for better prediction accuracy based on recent trends.

Two-Level Adaptive Predictor: A branch prediction method that uses two levels of history to make predictions: one for global history and another for local history, combining their outcomes for improved accuracy.