ARM SVE token decoding faces significant performance overhead from write-back cache coherency and store buffer flushing, with false sharing and cache line ping-pong effects potentially consuming 40% of memory bandwidth in multi-core scenarios. Memory barriers (DMB/DSB) necessary for ordering constraints add substantial latency penalties that compound when managing distributed KV-cache storage across cores.
Hardware-level write-back cache coherency represents a critical bottleneck in ARM SVE token decoding workloads, particularly when managing memory ordering constraints across multiple cores. The interaction between cache coherency protocols, store buffer flushing, and false sharing effects creates a complex micro-architectural challenge that directly impacts token generation throughput in large language model inference pipelines.
Modern ARM architectures implement multi-level cache hierarchies with each CPU core maintaining private L1 and L2 caches while sharing L3 caches [4]. Write-back cache coherency generates redundant traffic between L1 and L2 caches but provides significant benefits when transfers propagate to lower cache levels or memory [1]. However, this benefit comes at a cost: maintaining coherency across multiple cores requires continuous cache line tracking and invalidation cycles that directly impact store buffer utilization.
In token decoding scenarios, write-back coherency becomes problematic because token generation involves frequent updates to shared KV-cache structures. Each write operation must propagate through the cache hierarchy while respecting coherency invariants, creating serialization points that limit parallelism [19]. The store buffer—a small hardware queue that buffers pending write operations—becomes a critical resource under pressure, with flushes requiring explicit memory barriers to ensure ordering guarantees.
False sharing occurs when multiple cores modify different data elements that occupy the same 64-byte cache line [2, 3, 4]. Unlike true sharing where cores access identical data, false sharing creates unnecessary coherency traffic because unrelated modifications force entire cache lines to be transferred between cores. Research demonstrates concrete performance penalties that scale with contention levels [2].
Cache line ping-pong represents an extreme manifestation of false sharing where a single cache line continuously bounces between core caches [15, 17]. This effect is measurable through ping-pong microbenchmarks that quantify core-to-core latency by forcing cache line transfers [16]. In SVE token decoding, ping-pong becomes likely when multiple processing elements update adjacent positions in token buffers or attention metadata, as these structures typically align to cache line boundaries for SIMD efficiency. The penalty manifests as increased latency and reduced bandwidth utilization [17].
ARM's weakly-ordered memory model requires explicit barriers to enforce memory ordering when necessary [6, 10]. The Data Memory Barrier (DMB) ensures that all memory accesses in program order before the barrier complete before subsequent explicit data transfers [8]. The Data Synchronization Barrier (DSB) provides equivalent ordering semantics while also completing system-visible effects [6, 7].
In token decoding pipelines, memory barriers become mandatory when token generation stages must wait for KV-cache writes from previous stages. Each barrier operation stalls the store buffer, preventing new memory operations from issuing until prior operations complete [8]. The complexity of synchronization in multi-core systems scales with the number of participating cores and the frequency of ordering requirements [9]. For SVE operations processing multiple tokens in parallel, the cumulative barrier overhead can represent a significant fraction of total execution time, particularly when barriers are placed within inner loops.
Store buffers collect pending writes before they propagate to caches, providing temporary decoupling between execution units and the memory hierarchy. However, flushing these buffers—whether triggered by barriers, cache line evictions, or capacity limits—generates immediate traffic that must traverse the cache coherency protocol. Research on write-combining buffers shows that combining multiple 4-byte or 8-byte writes into 64-byte transfers can reduce traffic [14], but this optimization is difficult to apply to token decoding's irregular access patterns.
CRITICAL FINDING: Industry research indicates that cache coherency overhead can consume up to 40% of available memory bandwidth in large-scale multi-core systems [18]. For token decoding running on systems with 8-16 cores, this overhead directly reduces effective bandwidth available for KV-cache storage and retrieval operations.
ARMv8 architectures provide I/O coherency mechanisms [13] and specialized cache architectures designed to mitigate coherency overhead [20]. However, evaluations on representative ARM multi-cores (Phytium 2000+, ThunderX2) show that coherency penalties persist even with optimized implementations [20]. The fundamental issue stems from the weak memory model's requirement to explicitly order memory operations, which prevents aggressive speculation and pipelining at the hardware level.
Extended system coherency [5] addresses some limitations by allowing software to disable caching for specific regions, but this trades coherency overhead for even worse performance due to memory-level access penalties. For token decoding, this option is generally infeasible because KV-cache performance is critical to overall inference latency.
Recent work on accelerating token decoding through distributed KV-cache storage [19] exacerbates coherency challenges. When KV-cache entries are distributed across core-local memories or remote NUMA domains, coherency becomes genuinely global rather than local. Store buffer flushes must now propagate across interconnects, introducing additional latency. False sharing penalties intensify because cache lines containing distributed KV-cache entries must transfer between distant cores.
1. Barrier Latency: DMB/DSB instructions serialize store buffers, stalling execution pipelines for 10-50+ cycles depending on prior write density [6, 8]
2. Cache Line Transfers: Each false-shared or ping-pong cache line transfer costs 40-100 cycles to remote cores [16, 17]
3. Coherency Protocol Traffic: 40% bandwidth reduction in multi-core scenarios [18]
4. Write Buffer Stalls: Limited store buffer capacity (typically 32-64 entries) forces flushes when filling, creating unpredictable stall patterns
Optimizations exist at multiple levels: padding data structures to eliminate false sharing, batching operations to reduce barrier frequency, and using ARM-specific extensions like SVE with careful register scheduling to minimize memory operations. However, these approaches face fundamental limits. Token decoding's data-dependent nature prevents perfect batching, and SVE's vector length constraints limit padding effectiveness on systems with diverse hardware configurations.
Hardware-level write-back cache coherency and store buffer flushing create substantial overhead in ARM SVE token decoding, with false sharing and memory ordering constraints combining to reduce effective memory bandwidth by up to 40% on multi-core systems. The weak memory model's requirement for explicit barriers intensifies the problem, preventing hardware from speculatively hiding latency. For distributed KV-cache architectures, coherency overhead becomes genuinely global, fundamentally constraining token generation throughput. Practical solutions require careful algorithm design that minimizes shared memory accesses, strategic barrier placement, and potentially acceptance of slightly higher memory-level latency to reduce coherency traffic.