Efficient System-Enforced Deterministic Parallelism (1005.3450v1)

Abstract: Deterministic execution offers many benefits for debugging, fault tolerance, and security. Running parallel programs deterministically is usually difficult and costly, however - especially if we desire system-enforced determinism, ensuring precise repeatability of arbitrarily buggy or malicious software. Determinator is a novel operating system that enforces determinism on both multithreaded and multi-process computations. Determinator's kernel provides only single-threaded, "shared-nothing" address spaces interacting via deterministic synchronization. An untrusted user-level runtime uses distributed computing techniques to emulate familiar abstractions such as Unix processes, file systems, and shared memory multithreading. The system runs parallel applications deterministically both on multicore PCs and across nodes in a cluster. Coarse-grained parallel benchmarks perform and scale comparably to - sometimes better than - conventional systems, though determinism is costly for fine-grained parallel applications.

• The paper introduces Determinator, which enforces system-level deterministic execution for parallel programs to ensure reproducible outcomes.
• It employs isolated sandboxes, local naming, and explicit synchronization to eliminate shared state and timing dependencies.
• Evaluation shows coarse-grained workloads perform comparably to Linux while fine-grained tasks incur overhead, pointing to potential hardware support for future improvement.

Summary of "Efficient System-Enforced Deterministic Parallelism"

The paper introduces Determinator, a novel operating system designed for deterministic execution of parallel programs, ensuring that given identical inputs, a program's execution will yield consistent results across runs. This determinism is system-enforced, bypassing the nondeterministic challenges associated with traditional operating environments.

Key Contributions

Determinator Kernel Design

Determinator's kernel is grounded in several key principles:

1. Isolation Between Synchronization Points: Determinator's API eliminates traditional shared state abstractions, instead operating within isolated sandboxes and allowing interactions only at well-defined synchronization points. This design principle targets the removal of timing dependencies that typically result from shared state interactions.
2. Local Naming: The system relies on locally chosen, application-specific names rather than system-assigned global identifiers, thus avoiding timing-dependent naming issues common in conventional systems.
3. Explicit Synchronization Participants: Synchronization operations require explicit specification of the participating threads or processes, ensuring no timing dependencies influence the execution state.
4. Controlled Time Access: Determinator treats access to timing resources as I/O operations, thereby controlling the flow of time-dependent information.
5. Separation of Logic and Scheduling: To prevent applications from making timing-dependent decisions, scheduling is handled independently by the system, supporting an environment where application logic can remain unaffected by execution timing.

User-Level Runtime

Determinator's untrusted user-level runtime replicates familiar abstractions like Unix-style processes and file systems using distributed computing methodologies:

• Processes: Traditional process operations like fork, exec, and wait are emulated while maintaining local process ID namespaces to avoid global ID clashes.
• File Systems: The runtime maintains a weakly-consistent shared file system using versioning to handle state reconciliation between processes, thereby offering applications the semblance of shared state without physical sharing.
• Threading Models: A deterministic consistency (DC) memory model is utilized, ensuring deterministic scheduling of multithreaded operations via Snap and Merge operations.

Evaluation

Determinator was evaluated using several shared-memory parallel benchmarks on a multicore PC and across a distributed cluster setup. The results indicate that coarse-grained parallel workloads achieve performance comparable to Linux, with the potential for large speedups. Fine-grained applications, however, experience overhead due to the constraints of enforcing determinism, suggesting the current limitations of virtual memory-based approaches for such workloads.

Implications and Future Work

Despite current limitations, Determinator sets a foundational stage for deterministic systems. Its principles could potentially evolve to integrate into existing systems as deterministic sandboxes, providing safe, repeatable environments that are critical for debugging, fault-tolerance, and security against malicious software. The research also points towards the potential for hardware support to address limitations in supporting fine-grained parallelism.

Future directions would involve expanding the system's capabilities to handle more complex synchronization patterns, enhancing distributed memory support, and integrating a persistent storage layer—initiatives that could help bridge the gap between theoretical deterministic execution models and practical, real-world system applications.