CGGTY: Compiler-Guided Greedy Then Youngest

• CGGTY is a scheduling strategy that leverages compiler-based predictive analysis and beacon instrumentation to forecast workload phases on many-core machines.
• It employs static loop classification and polyhedral analysis to estimate execution time and memory footprints, enabling proactive resource management.
• Empirical results show up to 3.2x performance gains and a 76.78% average throughput improvement over traditional feedback-driven schedulers.

Compiler Guided Greedy Then Youngest (CGGTY) is a scheduling strategy developed in the context of many-core machines, leveraging compiler instrumentation and predictive modeling to optimize throughput and resource management. Unlike traditional feedback-driven schedulers, which respond reactively to resource contention, CGGTY proactively anticipates application demand by statically and dynamically forecasting workload phases. The approach hinges on two fundamental mechanisms: compiler-based static analysis of program loops and dynamic beacon-driven scheduling decisions, yielding significant improvements in throughput, cache utilization, and performance consistency across heterogeneous workloads.

1. Predictive Compiler Analysis and Loop Classification

CGGTY is rooted in compiler-assisted predictive analysis that targets the core computational structures of programs: loops. The compiler statically examines each loop, including nested and irregular forms, and classifies them according to the regularity of loop bounds and exit behavior, using four main categories (NBNE, NBME, IBNE, IBME). For regular loops, the iteration count is determined directly via normalization transformations such as LLVM’s loop-simplify pass. In contrast, irregular loops require the extraction of “critical” variables through Upwards Exposed Control Backslicing (UECB), which serve as features for training a predictive model (commonly decision trees or rule-based systems) to forecast loop trip counts at runtime.

Loop execution time is expressed via a closed-form linear model. For single loops: $T = \alpha \cdot N$ where $T$ is execution time and $N$ is the iteration count. For nested loops: $$T = c_0 + c_1 N_1 + c_2 (N_1 N_2) + \ldots + c_n (N_1 N_2 \ldots N_n)$$ with $c_1, c_2, \ldots, c_n$ estimated via linear regression and $N_1, \ldots, N_n$ representing trip counts at each nesting level. This model enables the compiler to encapsulate predicted timing in an execution beacon at loop-entry, providing the scheduler with quantitative forecasts.

2. Memory Footprint Estimation and Data-Reuse Analysis

Beyond execution timing, CGGTY incorporates memory footprint estimation using polyhedral analysis—an approach that constructs polyhedral access relations to enumerate distinct memory accesses considering loop bounds. Loops are further classified as “reuse” or “streaming” based on cache behavior metrics such as Static Reuse Distance (SRD). A loop with significant cache reuse is labeled as “reuse,” while those with minimal reuse are considered “streaming.” The beacon embeds these memory footprint estimates to inform the scheduler of anticipated cache and bandwidth demands, facilitating more sophisticated resource orchestration.

3. Beacon Instrumentation and Runtime Communication

The compiler instruments programs by inserting beacon function calls at strategic points, primarily at loop-entry and sometimes at hoisted interprocedural locations. Each beacon encapsulates:

• Predicted loop timing
• Trip count
• Estimated memory footprint
• Classification (reuse or streaming)

A completion beacon at loop-exit allows real-time correction or affirmation of compiler forecasts. During execution, all beacon data is written to a shared memory channel, which the scheduler polls continuously to aggregate workload phase forecasts across all running processes.

4. Proactive Beacon-Based Scheduling

At runtime, the scheduling framework employs a proactive strategy, leveraging beacon data to forecast upcoming resource contentions before they arise. The scheduler uses the closed-form timing model and memory footprint to estimate metrics such as expected memory bandwidth ($\mu_{bw} = F / T$, with $F$ as footprint and $T$ as timing). Based on these predictions, it dynamically selects an execution mode for each process:

• Reuse Mode: Staggers workloads to avoid concurrent high-reuse phases that could overwhelm the shared last-level cache.
• Stream Mode: Allows streaming workloads to run together, provided aggregate memory bandwidth does not exceed hardware capabilities.

This anticipatory scheduling stands in contrast to traditional feedback-driven approaches, such as the Completely Fair Scheduler (CFS), which detect and respond to contention only after performance degradation is manifest.

5. Comparative Performance and Resource Management

The CGGTY framework demonstrates substantial improvements in throughput and resource utilization. Empirical results indicate an average throughput increase of 76.78% over CFS, with performance gains up to 3.2x on the Amazon Graviton2 platform across 45 benchmarked consolidated workloads. These outcomes stem from minimizing interference, better co-location of processes based on predicted cache demands, and adaptive concurrency control. The proactive nature of CGGTY enables the avoidance of overlapping high-demand phases on shared resources, resulting in finer-grained dispatch and preemption policies that react before contention occurs.

Scheduling Approach	Method	Throughput Gain over CFS
CGGTY (Proactive)	Predictive Beacon	76.78% (avg), up to 3.2x
Traditional (Reactive/CFS)	Feedback-Driven	Baseline

These results underscore the efficacy of compiler-guided predictive approaches in many-core environments, particularly for heterogeneous and dynamically varying workloads.

6. Robustness and Applicability to Heterogeneous Workloads

Modern multi-tenancy environments are characterized by diverse workload behavior with input-dependent phases. CGGTY’s predictive, compiler-aware scheduling offers enhanced consistency over reactive schemes, which frequently underperform in the face of workload variability. By forecasting phase durations and resource requirements precisely, the scheduler manages resource allocation adaptively, maintaining optimal throughput and preventing cache thrashing or memory bandwidth oversubscription even as workload profiles shift.

7. Broader Implications for Many-Core Systems

The CGGTY methodology illustrates the potential of integrating static program analysis and runtime learning for system-level resource management. A plausible implication is the expansion of beacon-driven predictive scheduling for not only throughput optimization but also for other objectives like latency minimization and energy efficiency. This approach shifts the focus from reactive correction to proactive orchestration in resource-constrained, performance-sensitive, and heterogeneous environments. The use of proactive, compiler-guided scheduling represents a significant step towards more intelligent and granular control in many-core systems, fundamentally improving performance and utilization over legacy feedback-driven models.