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HPActor is a high-performance distributed framework based on the Actor model, designed specifically for building next-generation AI inference service platforms. It achieves million-level concurrency support through C++20 coroutines, lock-free schedulers, and a two-level Slab memory allocator, making it particularly suitable for long-running interactive agent workflows.
Section 01
HPActor is a high-performance distributed framework based on the Actor model, designed specifically for building next-generation AI inference service platforms. It achieves million-level concurrency support through C++20 coroutines, lock-free schedulers, and a two-level Slab memory allocator, making it particularly suitable for long-running interactive agent workflows.
Section 02
With the rapid development of Large Language Models (LLMs), the demand for AI inference services has undergone a fundamental shift. The traditional stateless request-response API model cannot meet the needs of long-running, stateful, multi-turn interactive agent workflows. HPActor emerged as a solution, based on the Actor model and implemented in C++20 to fully leverage modern hardware performance, with the goal of supporting million-level concurrent Actor scheduling and management.
Section 03
The Actor model was proposed by Carl Hewitt in 1973, with core features including state encapsulation, message passing, concurrent execution, and fault tolerance isolation. For AI inference services, this model is suitable for modeling user sessions, model instances, and tool calls as separate Actors, enabling state retention, lock-free communication, and fault tolerance isolation.
Section 04
Uses stackless coroutines; the HybridScheduler hybrid scheduler combines work stealing, adaptive victim selection, multi-level priority queues, EDF scheduling, and hierarchical time wheels.
Tier0 uses an mmap-based SegmentProvider; Tier1 uses a per-thread independent SlabCache to avoid fragmentation and lock contention.
Based on MPSC lock-free queues, supporting backpressure control and dead-letter queues.
Supports TCP/TLS, dynamic connection pools, and service discovery (UDP registration/Gossip protocol).
Erlang-style supervision tree, supporting OneForOne/AllForOne strategies and graceful shutdown mechanisms.
Section 05
Models user sessions as long-lived Actors, retaining conversation history and state to reduce repeated processing overhead.
Coroutines and the message system natively support LLM token-by-token streaming responses.
Models tools as independent Actors, supporting concurrent calls, timeout retries, and distributed deployment.
Provides metrics (OpenMetrics/Prometheus), distributed tracing (W3C TraceContext), and structured logging.
Section 06
Declares Actor trees and scheduler bindings via TOML files, supporting templates and AOT compilation.
CliActor provides a command-line interface, supporting functions like Actor list viewing and metric display.
Natively supports Protobuf serialization with zero-copy potential.
Section 07
| Feature | HPActor | Python asyncio | Java Akka | Ray |
|---|---|---|---|---|
| Language | C++20 | Python | Java/Scala | Python/C++ |
| Coroutine | C++20 stackless coroutine | asyncio | Not supported | Custom |
| Memory Management | Two-level Slab allocator | GC | JVM GC | Plasma/Custom |
| Scheduling | Hybrid scheduler + EDF | Event loop | Fork-Join | Distributed scheduling |
| Distributed | Built-in | Requires additional libraries | Akka Cluster | Ray Core |
| Supervision Tree | Fully supported | None | Fully supported | Limited |
| Performance | Million-level Actors | Ten-thousand-level | Hundred-thousand-level | Hundred-thousand-level |
| Application Scenario | Low-level inference engine | IO-intensive applications | Enterprise applications | ML workflows |
Section 08
HPActor combines C++20 features, lock-free data structures, memory management, and distributed support to provide a foundation for next-generation AI service platforms. It is currently in active development, with core systems, schedulers, and network layers already implemented. Future plans include adding cluster control, security architecture, and operation and maintenance planes. Developers of AI inference services requiring extreme performance, reliability, and scalability are advised to pay attention and give it a try.
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