Concurrent Computation Communication (C3)

• Concurrent Computation Communication (C3) is a framework that deliberately overlaps computation and communication to optimize throughput and resource utilization in concurrent systems.
• It builds on formal concurrency models, such as truly concurrent automata and process-algebraic constructs, to ensure correct synchronization of parallel and communicating tasks.
• C3 is applied in multi-GPU and distributed accelerator systems, employing strategies like schedule prioritization and resource partitioning to mitigate interference and boost performance.

Concurrent Computation Communication (C3) denotes frameworks and mechanisms by which computation and communication are deliberately overlapped in concurrent systems, with the aim of maximizing throughput and hardware resource utilization. While its most impactful applications have been realized in multi-GPU systems for AI and HPC workloads, C3’s conceptual foundations span concurrency theory, distributed process calculi, and hardware/software co-design. C3 is directly motivated by the increasing dominance of data movement latency in largescale model training and graph/data-centric workloads, necessitating architectural and algorithmic advances at the boundary of hardware, system software, and mathematical concurrency models.

1. Theoretical Foundations of C3

C3 as an abstract framework is rigorously captured in concurrency theory, particularly through truly concurrent automata (TCA) and process-algebraic constructs that formalize the interplay between independent computation tasks and explicit communication events. In the TCA model, events are represented through pomsets—a finite partially ordered set $(X, \le, \ell)$ where $\ell: X \to \Sigma$ assigns an action label from the alphabet $\Sigma$. Parallelism and communication are distinguished by specific operators: $\parallel$ for parallel composition and $\,\mid\,$ for communication-merge, the latter synchronizing send ($a!$) and receive ($a?$) pairs into internal actions (τ) (Wang, 4 Sep 2024).

C3-algebra extends Kleene algebra with these concurrent and communication operators, governed by compositional laws such as

$$(a\parallel b)\parallel c = a\parallel(b\parallel c), \,\, a\mid b = b\mid a,\,\, (a\parallel b)\mid c = (a\mid c)\parallel b$$

and a derived “full concurrency” operation combining all forms of interaction. Correctness is established via language equivalence (set of execution traces) and pomset bisimulation, with completeness and full abstraction theorems linking syntactic equational reasoning to semantic behavior.

2. Formal Abstractions and Communication Mechanisms

The C3 framework admits orthogonal axes of communication—directionality (uni/bidirectional) and implementation style (direct via rendezvous/message-passing, indirect via shared memory). Each pattern introduces distinct correctness challenges, particularly concerning mutual exclusion, deadlock, and message integrity (Diertens, 2011).

Typical abstractions include:

• Direct, unidirectional (message passing): Modeled as synchronous or asynchronous channels with clear send/receive atomicity.
• Indirect, unidirectional (shared location): Guarded by lock-management or status flag encapsulation to ensure safe and sequenced handoff.
• Bidirectional and undirected (shared memory): Greater risk of race conditions and overwrites, mitigated by formal encapsulation and higher-level interface abstractions.

Encapsulation of the communication “region” (data, flags, locks) and promotion to a first-class concurrent function/interface are canonical C3 solutions, preserving safety (no lost or stale updates), liveness (no deadlock), and transparency from the client’s perspective.

3. C3 in GPU and Distributed Accelerator Architectures

C3 is operationalized on modern GPUs by launching compute (typically large GEMM kernels) and communication (collective operations such as AllReduce or AllGather) on distinct hardware or logical streams, leveraging the concurrency of SMs and DMA engines (Hong et al., 28 Apr 2025, Agrawal et al., 18 Dec 2024, Kurzynski et al., 13 Nov 2025). In frameworks such as PyTorch’s FSDP or DeepSpeed, this overlap is fundamental for efficient tensor-parallel (TP) or expert-parallel (MoE) workflows.

Guaranteed concurrency relies on:

• Hardware capability for at least two concurrent kernel types (compute and copy/DMA).
• Sufficiently independent scheduling of streams to avoid starvation and resource contention.
• High-bandwidth interconnects (NVLink, PCIe, Infinity Fabric).

On the software side, standard collective libraries (NCCL, RCCL) are orchestrated to synchronize with compute workloads, with careful partitioning to maximize overlap and minimize synchronization overhead.

4. Performance Models, Interference, and Optimization

The performance benefit of C3 is modeled by comparing serial execution time $$T_{\mathrm{serial}} = T_{\mathrm{compute}} + T_{\mathrm{comm}}$$ to ideally-overlapped time $$T_{\mathrm{ideal}} = \max(T_{\mathrm{compute}}, T_{\mathrm{comm}})$$. Empirically, naive C3 achieves only a modest fraction of this ideal (e.g., $21\%$) due to compute and memory interference—compute kernels cede resources when running in parallel with communication, and both share caches and memory bandwidth, constraining overlap (Agrawal et al., 18 Dec 2024).

Tabulated empirical results highlight the effect of various optimizations:

Configuration	Avg. % of Ideal Speedup
Baseline C3 (no SP/RP)	21%
C3 + Schedule Prioritization (SP)	42%
C3 + Resource Partitioning (RP)	41%
ConCCL (DMA engine offload)	66%
ConCCL + RP (memory-bound GEMMs)	72%

• Schedule Prioritization (SP): Communication kernels are launched before compute kernels to ensure prompt allocation of required compute units.
• Resource Partitioning (RP): Explicit allocation of compute units to comm/compute streams, often with a heuristic informed by measured slowdown factors.
• DMA Engine Offload (ConCCL): Communication tasks are assigned to SDMA engines, isolating compute from communication and greatly narrowing the realized-to-ideal speedup gap.

Contemporary designs such as FlashOverlap introduce tile-granular readiness/tile-group signaling mechanisms, achieving up to $1.65\times$ speedup and $98\%$ overlap efficiency, highlighting the benefit of minimizing interference while maintaining agnosticism to communication primitive choice (Hong et al., 28 Apr 2025).

5. Case Study: C3 in Processing-in-Memory (PIM) and DRAM Architectures

Shared-PIM extends C3 principles intra-DRAM, engineering concurrent execution between memory-mapped computation (PIM LUTs) and data transfer (bank-wise and subarray-wise communication) (Mamdouh et al., 28 Aug 2024). This is achieved via additional bank-level buses, sense amplifiers, and shared rows that double as staging registers for data movement. Compute-rows proceed on local operations, while shared rows and the BK-bus enable pipelined, fully-overlapped inter-subarray transfers.

Quantitatively, Shared-PIM reduces row-move latency by $5\times$ compared with baseline LISA architecture and delivers $1.4\times$–$1.44\times$ speedup in matrix and polynomial multiplication workloads with negligible ($7.16\%$) chip area penalty.

6. Node-Scale Dynamics: C3 and System-Level Effects

Recent observation of the Lit Silicon phenomenon exposes non-obvious performance variability in multi-GPU nodes arising from the coupling of C3 with thermal/DVFS dynamics (Kurzynski et al., 13 Nov 2025). Here, thermal imbalances result in “straggler” GPUs with reduced clock rates, breaking the homogeneity assumption in synchronized C3 patterns. This propagates as node-level throughput variability (up to $4\%$), observable as a “straggler wave” cycling through different compute and communication phases.

Mitigation is achieved using lightweight instrumentation to monitor per-GPU overlap ratios and dynamically reallocate power caps (GPU-Red, GPU-Realloc, CPU-Slosh policies), leading to up to $6\%$ performance and $4\%$ power efficiency improvements—translating to major datacenter cost savings. These techniques require minimal code changes and are orthogonal to underlying frameworks or hardware—a plausible implication is that system-level C3 optimization increasingly demands dynamic, power-aware scheduling and cross-stack integration.

7. Open Challenges and Prospects

Several directions are identified as necessary for advancing the state of C3:

• Hardware architectural support: More granular compute/memory bandwidth partitioning, more capable DMA engines (support for arithmetic for AllReduce offload), and QoS/tagging for isolation.
• Software runtime: Finer-granularity scheduling for more than two concurrent streams, better coherence/caching management when DMA and compute overlap, and dynamic scheduling responsive to workload power/thermal constraints.
• Abstractions and verification: Ensuring higher-level process-algebraic C3 models map correctly to hardware implementations, particularly concerning atomicity and safety in shared and distributed memory settings.
• Scalability: Extending intra-node C3 designs to multi-node, interconnect, and networked settings, incorporating RDMA/NIC offload and non-blocking collective communication.

The convergence of formal C3 models and pragmatic system designs has now demonstrably influenced both algorithmic speedups and infrastructure efficiency, but continued performance scaling will likely depend on joint advances in hardware, runtime software, and formal reasoning about concurrent interaction.