In our work to bring F# to systems programming, we’re pursuing a vision of sophisticated memory management outside the familiar boundaries of the .NET runtime. For developers who have only known garbage collection as an omnipresent runtime service, the concept we’re pursuing, extracting garbage collection algorithms and adapting them for zero-runtime environments, represents a significant departure from established patterns. Our current research focuses on how three complementary systems might work together: SGen’s proven generational collection algorithms, the Olivier actor model we’re developing, and our proposed Prospero orchestration layer.
This blog entry examines how these three components could form an integrated whole, where memory management strategies are tailored at compile time to match actor-based application architectures. We believe that by statically binding an enhanced version of SGen into actor-based systems, we can bring sophisticated memory management to systems programming while maintaining the deterministic performance characteristics that real-time applications demand. These ideas remain in the formative stage, but our initial investigations show promise.
Three Systems in Concert: A Design Vision
Effective memory management in a zero-runtime environment requires more than simply porting existing garbage collection algorithms. In traditional runtime environments, garbage collection operates as a global service treating all allocations uniformly. This approach, while suitable for general-purpose applications, fails to exploit the structured nature of actor-based systems. Our design proposes a different philosophy.
The Olivier actor model, which takes lessons from Erlang, would provide the organizational structure that makes sophisticated memory management practical without runtime support. In our design, actors aren’t merely concurrent entities; they represent natural boundaries for memory ownership and lifecycle management. Each actor would have a clear birth, lifetime, and death, creating a temporal structure that garbage collection algorithms could exploit. When an actor terminates, we can know with certainty that all its private state can be reclaimed, knowledge that would be difficult to derive in an unstructured system.
Prospero, our proposed orchestration layer, would transform this actor structure into actionable memory management strategies. Beyond scheduling actor execution, Prospero would coordinate memory allocation, collection policies, and cross-actor references. Our design envisions Prospero understanding that a UI actor processing frequent small messages has fundamentally different memory patterns than a data processing actor handling large batches. This understanding would enable targeted collection strategies that would be impractical with a monolithic garbage collector.
SGen provides the algorithmic foundation, the generational hypothesis and mark-and-sweep algorithms refined over more than a decade of development. However, our approach wouldn’t merely port SGen; we propose fundamental enhancements to make it actor-aware. The static binding process we’ve designed wouldn’t just link SGen as a library but would specialize its algorithms for the specific actor topology of each application.
Designing Actor-Aware Memory Architecture
Our current architectural exploration centers on a key design principle: each process would own a heap managed by an actor-aware version of SGen, with actors within that process receiving dedicated regions within that heap. We believe this design could provide an optimal balance between isolation and efficiency:
module Fidelity.Memory.Design
open Alloy.NativeInterop
open Olivier
open Prospero
type ProcessConfiguration = {
Name: string
HeapSize: uint64
ActorCapacity: int
CollectionPolicy: CollectionPolicy
}
and CollectionPolicy =
| Aggressive // Would collect after each actor termination
| Balanced // Would collect based on thresholds
| Relaxed // Would collect only under pressure
// Envisioned native bindings to enhanced SGen
module SGenActorAware =
let createProcessHeap =
dllImport<uint64 -> nativeptr<ProcessConfig> -> nativeint>
"sgen_actor" "sgen_create_process_heap"
let allocateActorRegion =
dllImport<nativeint -> uint64 -> uint64 -> nativeint>
"sgen_actor" "sgen_allocate_actor_region"
let markActorTerminated =
dllImport<nativeint -> uint64 -> unit>
"sgen_actor" "sgen_mark_actor_terminated"
This design proposes that when Prospero creates a process, it would initialize an SGen heap specifically configured for that process’s expected workload. Within this heap, each actor would receive a dedicated region, not a separate heap, but a designated area that SGen could track independently.
Prospero’s Role: Orchestrating Memory
In our current design thinking, Prospero would serve as more than a simple scheduler. We envision it as an intelligent orchestrator that understands the relationship between actor behavior and memory patterns. This understanding would drive sophisticated collection strategies:
module Prospero.MemoryOrchestration.Design
type ActorMemoryLifecycle = {
ActorId: uint64
Region: nativeint
AllocationPattern: AllocationPattern
CollectionHint: CollectionHint
}
and AllocationPattern =
| Ephemeral // Short-lived objects, frequent allocation
| Stable // Long-lived objects, infrequent allocation
| Mixed // Combination of patterns
and CollectionHint =
| CollectOnTermination
| CollectOnThreshold of threshold: uint64
| CollectNever
let createActor<'T when 'T :> Actor<'Message>> (hint: AllocationPattern) =
// Prospero would use the hint to configure SGen's behavior
let region = match hint with
| Ephemeral ->
// Small region, frequent collection
SGenActorAware.allocateActorRegion heap actorId (10UL * MB)
| Stable ->
// Large region, infrequent collection
SGenActorAware.allocateActorRegion heap actorId (100UL * MB)
| Mixed ->
// Balanced configuration
SGenActorAware.allocateActorRegion heap actorId (50UL * MB)
// Actor would be created with region association
Olivier.spawnWithRegion<'T> region
This tight integration between orchestration and memory management could enable optimizations impossible in traditional systems. Prospero could observe actor behavior over time and adjust memory strategies dynamically, all while maintaining the zero-runtime principle through compile-time specialization.
Static Binding: The Crucial Innovation
The mechanism that would make this integration possible is our proposed approach to static library binding. Rather than viewing SGen as an inseparable part of a runtime, we’re analyzing how to treat it as a C library implementing sophisticated algorithms that can be adapted at compile time:
// Exploration of compile-time transformation
module CompileTimeIntegration.Design
// Developer would write standard actor code
type DataProcessor() =
inherit Actor<DataMessage>()
let mutable cache = Map.empty<string, ProcessedData>
override this.Receive message =
match message with
| Process data ->
let result = performComplexProcessing data
cache <- Map.add data.Id result cache
| Retrieve id ->
Map.tryFind id cache |> ReplyChannel.send
// Firefly compiler would generate actor-aware code
type DataProcessor_Compiled() =
inherit Actor<DataMessage>()
// Compiler would identify this as actor state
let mutable cache = Map.empty<string, ProcessedData>
let cacheRoot = SGenActorAware.registerActorRoot this.Region (AddressOf(cache))
override this.Receive message =
match message with
| Process data ->
// Allocation would happen in actor's region
let result = performComplexProcessing data
// Write barrier for generational collection
SGenActorAware.writeBarrier this.Region (AddressOf(cache))
cache <- Map.add data.Id result cache
| Retrieve id ->
Map.tryFind id cache |> ReplyChannel.send
interface IDisposable with
member this.Dispose() =
// Prospero would coordinate with SGen for cleanup
SGenActorAware.markActorTerminated this.Region
This transformation illustrates our vision for how compile-time analysis could replace runtime introspection. The Firefly compiler would identify actor state, determine allocation patterns, and generate appropriate SGen integration code, all without runtime overhead.
Cross-Process References
One of the most innovative aspects of our design is how actor systems naturally extend beyond single-process boundaries. Traditional garbage collectors struggle with distributed references, but our actor-based approach provides a streamlined solution:
module CrossProcessReferences.Design
[<Struct>]
type ReferenceSentinel = {
ProcessId: uint64
ActorId: uint64
mutable State: ReferenceState
mutable LastVerified: int64
}
and ReferenceState =
| Valid
| ActorTerminated
| ProcessUnavailable
| Unknown
// Proposed integration with message passing
let sendCrossProcess (sender: ActorRef) (target: ActorRef) message =
match target.Location with
| LocalProcess ->
// Direct delivery through shared heap
Olivier.deliverLocal target message
| RemoteProcess sentinel ->
// Verify through Prospero's distributed protocol
match Prospero.verifySentinel sentinel with
| Valid ->
let data = BAREWire.serialize message
Prospero.sendRemote sentinel.ProcessId data
| ActorTerminated ->
sender.Tell(DeliveryFailed(target, ActorNoLongerExists))
| ProcessUnavailable ->
Prospero.scheduleRetry sentinel message
This design would provide rich information about reference validity without requiring global coordination or stop-the-world pauses. The sentinel mechanism we’re making part of our actor model is designed to enable sophisticated failure handling while maintaining the actor model’s isolation guarantees.
Memory Patterns in Practice
To illustrate how these ideas might work together, consider this design exploration for a real-time analytics system:
module AnalyticsSystem.Design
// High-frequency data ingestion actor
type IngestionActor() =
inherit Actor<IngestMessage>()
// Prospero would configure for ephemeral allocation
let config = { AllocationPattern = Ephemeral
CollectionHint = CollectOnThreshold(5UL * MB) }
override this.Receive message =
match message with
| DataPoint point ->
let validated = validateDataPoint point
let transformed = transformForAnalysis validated
AnalysisRouter.Tell(Analyze transformed)
// Long-lived aggregation actor
type AggregationActor() =
inherit Actor<AggregateMessage>()
// Prospero would configure for stable allocation
let config = { AllocationPattern = Stable
CollectionHint = CollectOnTermination }
// Long-lived state in major heap
let mutable aggregates = MetricAggregates.empty
override this.Receive message =
match message with
| UpdateMetric metric ->
aggregates <- MetricAggregates.update aggregates metric
| QueryMetric query ->
let result = MetricAggregates.query aggregates query
ReplyChannel.send result
This example illustrates how different actors within the same process could have radically different memory patterns, and how our proposed system would accommodate these differences through compile-time configuration and runtime specialization.
Implications and Future Directions
The integration of SGen, Olivier, and Prospero represents more than a technical exercise; it suggests a new paradigm for systems programming where sophisticated runtime services become compile-time transformations. Our research indicates several promising directions:
Compile-Time Specialization: By analyzing actor topologies at compile time, we are confident the Firefly compiler could generate specialized memory management code for each application, eliminating the one-size-fits-all overhead of traditional garbage collectors.
Hardware Integration: Modern processors increasingly include hardware support for memory management. Our compile-time approach could potentially leverage these features more effectively than traditional runtime collectors.
Formal Verification: With the entire memory management strategy visible at compile time, we envision opportunities for formal verification of memory safety properties, providing guarantees impossible with runtime-based systems.
Adaptive Optimization: Prospero’s orchestration role could extend to profile-guided optimization, where production behavior informs recompilation with refined memory strategies.
Conclusion: Actor-Aware Memory Management
The design we’re pursuing represents a fundamental rethinking of how garbage collection can work in systems programming. By recognizing that actors provide natural boundaries for memory management, and by leveraging compile-time analysis to specialize collection strategies, we believe it’s possible to bring sophisticated memory management to domains where runtime garbage collection has been impractical.
This integration of SGen’s proven algorithms, our Olivier actor model and Prospero’s orchestration design isn’t just about making garbage collection work without a runtime. It’s about demonstrating that the boundaries between systems programming and managed programming are more fluid than traditionally assumed. Through careful design and innovative compilation techniques, we can envision a future where F# truly serves as a language for all seasons, from embedded devices to distributed systems, unified by an approach to memory management that adapts to each environment while preserving the elegant functional programming model that makes F# so powerful.
We are excited to develop these concepts as we work to refine our designs into practical framework elements. We’re not just building new tools; we’re charting a new course for programming languages to meet the demands of modern computing while maintaining the safety and expressiveness that developers have come to expect. The journey from concept to implementation will undoubtedly reveal new challenges and opportunities, but our initial explorations suggest that actor-aware, compile-time garbage collection represents a promising direction for the future of systems programming in functional languages.