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SyncFlow
A Modular Synchronization Framework for AI Compute
SyncFlow is an open source specification for efficient, adaptive synchronization across AI processors. It provides a modular coordination framework for managing processor synchronization, workload distribution, memory coordination, dependencies, communication, execution, and resource utilization across heterogeneous AI compute environments.
The specification is designed to minimize unnecessary synchronization, data movement, processor idle time, communication overhead, and energy consumption while maintaining the consistency and execution requirements of AI workloads.
Design Principles
- Modular architecture with independently implementable components
- Hardware-agnostic processor coordination
- Efficient synchronization with minimal unnecessary barriers
- Asynchronous execution where dependencies permit
- Adaptive synchronization based on workload and system conditions
- Intelligent coordination across heterogeneous processors
- Efficient memory and data movement
- Dynamic workload balancing
- Dependency-aware execution
- Priority-aware resource allocation
- Energy-aware synchronization and communication
- Fault tolerance and recovery
- Local and distributed compute support
- Human-defined execution and consistency requirements
- Vendor-neutral interoperability
- Extensible plugin architecture
Core Modules
Processor Discovery
The Processor Discovery Module identifies and maintains the capabilities and operational state of available processors.
Features include:
- Processor identification
- Processor type and capability discovery
- Compute capacity reporting
- Memory capacity reporting
- Supported precision and execution capabilities
- Accelerator discovery
- Interconnect capabilities
- Synchronization capabilities
- Processor utilization monitoring
- Processor availability tracking
- Runtime capability updates
Topology Management
The Topology Management Module maintains a representation of processor, memory, and communication relationships.
Features include:
- Processor topology mapping
- Memory locality mapping
- Communication path discovery
- Bandwidth measurement
- Latency measurement
- Peer accessibility detection
- Interconnect capability tracking
- Communication cost estimation
- Synchronization path optimization
- Congestion awareness
- Reliability-aware topology management
Synchronization Engine
The Synchronization Engine manages synchronization between processors, workloads, memory regions, and execution stages.
Features include:
- Processor synchronization
- Peer synchronization
- Group synchronization
- Pipeline synchronization
- Event-based synchronization
- Dependency-aware synchronization
- Collective synchronization
- Partial synchronization
- Asynchronous synchronization
- Synchronization completion tracking
- Synchronization timeout handling
- Synchronization cancellation
The system MUST avoid global synchronization when a narrower synchronization scope satisfies the workload requirements.
Synchronization Intelligence
The Synchronization Intelligence Module determines when synchronization is necessary and selects the most efficient synchronization strategy.
Features include:
- Synchronization requirement analysis
- Synchronization elimination
- Predictive synchronization
- Partial synchronization
- Dependency prediction
- Synchronization batching
- Synchronization coalescing
- Adaptive synchronization strategies
- Synchronization cost analysis
- Synchronization frequency optimization
- Critical-path synchronization
- Workload-aware synchronization
The system SHOULD synchronize only the state and processors required to satisfy declared execution dependencies.
Dynamic Load Balancing
The Dynamic Load Balancing Module distributes computational work according to processor capability, workload conditions, and runtime availability.
Features include:
- Runtime workload measurement
- Processor utilization monitoring
- Dynamic task assignment
- Processor-aware task placement
- Work stealing
- Queue balancing
- Heterogeneous processor balancing
- Load migration
- Priority-aware redistribution
- Capacity-aware scheduling
- Runtime workload rebalancing
Dependency Management
The Dependency Management Module tracks relationships between computations, data, processors, and synchronization requirements.
Features include:
- Execution dependency graphs
- Data dependency tracking
- Processor dependency tracking
- Dependency resolution
- Dependency ordering
- Completion tracking
- Dependency propagation
- Dependency-aware scheduling
- Dependency change detection
- Dependency optimization
Versioned State
The Versioned State Module manages changes to computational state and enables efficient synchronization of modified data.
Features include:
- Versioned tensor state
- Versioned model state
- Versioned memory regions
- State change tracking
- Incremental synchronization
- Delta synchronization
- Stale-state detection
- Conflict detection
- State reconciliation
- Consistency validation
- State history tracking
Memory Coordination
The Memory Coordination Module manages data residency and movement across processor-accessible memory resources.
Features include:
- Local memory coordination
- Shared memory coordination
- Peer memory coordination
- Distributed memory coordination
- Replicated memory coordination
- Memory residency tracking
- Memory migration
- Memory invalidation
- Memory prefetching
- Memory pressure management
- Data locality optimization
Supported memory states SHOULD include:
- CLEAN
- DIRTY
- READ_ONLY
- WRITE_PENDING
- WRITE_COMPLETE
- INVALID
- MIGRATING
- REPLICATED
Communication Layer
The Communication Layer provides an abstraction for transferring data and synchronization information between processors.
Features include:
- Processor-to-processor communication
- Peer-to-peer communication
- Shared-memory communication
- Interconnect communication
- Network communication
- Transport selection
- Communication routing
- Bandwidth optimization
- Latency optimization
- Data batching
- Data compression
- Delta transfers
- Communication prioritization
The communication layer MUST remain independent of any specific hardware vendor or implementation.
AI Compute Synchronization
The AI Compute Synchronization Module provides synchronization primitives specific to AI workloads.
Features include:
- Tensor synchronization
- Model synchronization
- Parameter synchronization
- Gradient synchronization
- Optimizer-state synchronization
- Embedding synchronization
- KV-cache synchronization
- Inference-state synchronization
- Batch-state synchronization
- Distributed checkpoint coordination
- Model parallel synchronization
- Tensor parallel synchronization
- Pipeline parallel synchronization
- Expert parallel synchronization
Execution Scheduler
The Execution Scheduler coordinates task execution across available processors.
Features include:
- Processor selection
- Task scheduling
- Dependency-aware scheduling
- Locality-aware scheduling
- Asynchronous execution
- Compute and communication overlap
- Processor affinity
- Task prioritization
- Runtime rescheduling
- Workload-aware scheduling
- Resource-aware scheduling
Event Management
The Event Management Module provides event-driven coordination between processors and execution components.
Features include:
- Synchronization events
- Completion events
- Processor events
- Dependency events
- Event queues
- Event propagation
- Event cancellation
- Event timeout handling
- Event dependency tracking
- Event prioritization
Synchronization SHOULD be event-driven whenever polling can be avoided.
Priority Management
The Priority Management Module assigns and manages synchronization, workload, and resource priorities.
Features include:
- Synchronization priorities
- Workload priorities
- Critical-path prioritization
- Latency-sensitive synchronization
- Background synchronization
- Priority inheritance
- Priority escalation
- Priority-based resource allocation
Supported priority levels MAY include:
- CRITICAL
- HIGH
- NORMAL
- LOW
- BACKGROUND
Energy Management
The Energy Management Module optimizes synchronization and communication according to energy consumption and processor conditions.
Features include:
- Processor power monitoring
- Communication energy estimation
- Synchronization energy estimation
- Energy-aware processor selection
- Energy-aware communication selection
- Low-power synchronization
- Performance-per-watt optimization
- Energy budget management
- Thermal-aware coordination
- Energy-aware workload placement
Resource Arbitration
The Resource Arbitration Module coordinates access to processors, memory, communication resources, and other computational resources.
Features include:
- CPU resource allocation
- GPU resource allocation
- NPU resource allocation
- Accelerator resource allocation
- Memory allocation
- Communication resource allocation
- Resource reservations
- Contention management
- Resource prioritization
- Dynamic resource reassignment
Fault Management
The Fault Management Module detects synchronization and processor failures and coordinates recovery.
Features include:
- Processor failure detection
- Communication failure detection
- Synchronization timeout detection
- Stale-state detection
- Failed-operation recovery
- Processor failover
- State recovery
- Partial workload recovery
- Synchronization retry
- Recovery coordination
Telemetry and Observability
The Telemetry and Observability Module provides visibility into synchronization and compute performance.
Features include:
- Synchronization latency measurement
- Synchronization frequency measurement
- Processor utilization
- Communication latency
- Communication volume
- Memory movement
- Queue latency
- Processor idle time
- Synchronization failures
- Workload distribution
- Energy consumption
- Performance metrics
- Resource utilization metrics
Synchronization Contracts
Workloads SHOULD declare synchronization requirements through synchronization contracts.
A synchronization contract MAY define:
- Operation identifier
- Processor requirements
- Synchronization scope
- Data dependencies
- Required state
- Consistency requirements
- Priority
- Latency requirements
- Communication preferences
- Energy preferences
- Completion requirements
The runtime SHOULD use these requirements to determine:
- Processor placement
- Memory placement
- Communication method
- Synchronization primitive
- Synchronization timing
- Synchronization scope
- Amount of data transferred
- Whether synchronization can be eliminated
Synchronization Tokens
SyncFlow MAY use synchronization tokens to represent synchronization state without requiring continuous polling.
A synchronization token MAY contain:
- Operation identifier
- Processor identifier
- Sequence identifier
- Memory scope
- Dependency set
- Completion state
- Timestamp
- Synchronization priority
- Consistency state
Tokens SHOULD support event-driven completion detection and dependency propagation.
Synchronization Budget
Implementations SHOULD maintain a synchronization cost model.
The synchronization budget MAY account for:
- Barrier cost
- Data transfer cost
- Processor waiting cost
- Memory coordination cost
- Scheduling cost
- Communication cost
- Recovery cost
- Energy cost
The synchronization engine SHOULD select strategies that satisfy workload requirements while minimizing total synchronization cost.
Adaptive Synchronization
SyncFlow implementations SHOULD adapt synchronization behavior according to runtime conditions.
Adaptation MAY consider:
- Communication latency
- Available bandwidth
- Processor count
- Processor utilization
- Dependency density
- Workload independence
- Memory locality
- Data size
- Synchronization frequency
- Energy availability
- Thermal conditions
- Failure conditions
The system SHOULD prefer the narrowest synchronization scope capable of satisfying the workload.
Processing Models
SyncFlow SHOULD support multiple execution models, including:
- Synchronous execution
- Asynchronous execution
- Event-driven execution
- Pipeline execution
- Parallel execution
- Distributed execution
- Heterogeneous execution
- Dependency-driven execution
Implementations MAY combine multiple execution models within a single workload.
Collective Coordination
The framework SHOULD support collective coordination operations where required by workloads.
Supported operations MAY include:
- Broadcast
- Reduce
- All-reduce
- All-gather
- Reduce-scatter
- Scatter
- Gather
- Point-to-point synchronization
Collective operations SHOULD be selected and optimized according to topology, workload requirements, data size, processor availability, and communication cost.
Efficiency Requirements
A SyncFlow implementation MUST seek to minimize:
- Unnecessary synchronization
- Unnecessary data movement
- Processor idle time
- Communication overhead
- Memory movement
- Redundant state transfers
- Scheduling overhead
- Energy consumption
An implementation SHOULD maximize:
- Processor utilization
- Data locality
- Compute and communication overlap
- Useful parallelism
- Synchronization efficiency
- Resource utilization
- Performance per watt
Interoperability
SyncFlow is designed to coordinate heterogeneous AI processors without requiring a specific processor architecture.
Implementations SHOULD support interoperability between different classes of processors and accelerators when their capabilities permit coordinated execution.
The specification MUST NOT require dependence on a single hardware vendor, processor architecture, or execution environment.
Optional Plugin Modules
Distributed Cluster Plugin
Provides coordination across multiple compute nodes.
Features include:
- Multi-node synchronization
- Cluster topology management
- Distributed processor coordination
- Cross-node dependency management
- Distributed workload balancing
- Network-aware synchronization
Training Plugin
Provides synchronization capabilities for distributed AI training.
Features include:
- Gradient synchronization
- Parameter synchronization
- Optimizer-state synchronization
- Gradient accumulation
- Distributed training coordination
- Training-state consistency
Inference Plugin
Provides synchronization capabilities optimized for AI inference.
Features include:
- Low-latency synchronization
- Request coordination
- Dynamic batching
- Inference-state synchronization
- KV-cache coordination
- Multi-processor inference
Model Sharding Plugin
Provides coordination for distributed model execution.
Features include:
- Tensor parallelism
- Pipeline parallelism
- Expert parallelism
- Parameter sharding
- Model-state coordination
- Shard placement optimization
Security Plugin
Provides security controls for processor and synchronization coordination.
Features include:
- Processor authentication
- Synchronization authorization
- Secure state exchange
- Communication protection
- Processor isolation
- Access control
- Integrity validation
Deterministic Execution Plugin
Provides deterministic execution and synchronization behavior.
Features include:
- Deterministic scheduling
- Deterministic synchronization
- Execution ordering
- Reproducibility controls
- Deterministic state management
- Reproducible distributed execution
Checkpoint Plugin
Provides distributed and recovery-oriented checkpoint coordination.
Features include:
- Distributed checkpoints
- Incremental checkpoints
- Versioned checkpoints
- State-aware checkpoints
- Recovery checkpoints
- Checkpoint synchronization
- Partial recovery
Specification Branding License (SBL)
Standard
- Fully AGPL-3.0+ compliant system
- Copyleft enforced for network deployments
- Required attribution:
- Roxanne Ardary
- https://www.roxanneardary.com/
Optional
- Specification Branding License (SBL)
- Attribution-free commercial deployment
- Pricing based on scale, usage, and deployment scope
- https://roxanneardary.com/syncflow/
License & Notice Requirements
SyncFlow is released under the GNU Affero General Public License v3.0 or later (AGPL-3.0+).
By contributing to any Open Arsenal project, you agree that your contributions will also be released under this license.
Please note the following:
- All contributions must comply with the AGPL-3.0+ terms.
- Under Section 7 of the license, all redistributions, forks, and derivative works must preserve attribution to Roxanne Ardary and roxanneardary.com.
- SyncFlow specifications are free to use with attribution. A Specification Branding License can be negotiated upon request.
- The project’s
notice.mdfile tracks attribution requirements and contributor acknowledgments. Any update that adds new contributors or modifies attribution should also updatenotice.md. - When submitting a pull request, ensure that any new files maintain the attribution headers where applicable.
- Network-deployed versions of this software must also remain fully AGPL-3.0+ compliant, including exposure of source code modifications when applicable under the license.
For full legal details, please refer to the AGPL-3.0+ license and the project’s notice.md file.
Notice – SyncFlow
Attribution Requirement: Under Section 7 of the AGPL 3.0+ license, all redistributions, forks, and derivative works, including network-deployed versions of this project, must provide attribution to Roxanne Ardary and roxanneardary.com.
Contributors
This file tracks contributors and their specific contributions to the project.
- Roxanne Ardary, roxanneardary.com – September 8, 2026
Created the repository for SyncFlow. Created the specification for a modular synchronization framework for AI compute, including efficient coordination and synchronization across AI processors. - [Add other contributors here] – [Date]
[Describe contribution in one sentence]
License – SyncFlow
This repository is licensed under the GNU Affero General Public License v3.0 or later (AGPL-3.0+).
Key Points
- You are free to use, modify, and distribute the code.
- All redistributions, forks, and derivative works or network-deployed versions must also be licensed under AGPL-3.0+ and provide attribution to Roxanne Ardary and roxanneardary.com as required under Section 7 of the license.
- The software is provided “as is,” without warranty of any kind.
For the full license text, see GNU AGPL-3.0 License.