Highload on ThecoreGrid focuses on designing and operating systems that handle massive scale, traffic, and data under strict reliability requirements.
We explore architectures and patterns for horizontal scaling, load distribution, fault tolerance, and performance optimization in distributed environments. Topics include sharding, replication, caching strategies, queueing systems, backpressure handling, and latency reduction under peak load. We analyze real-world trade-offs between consistency, availability, and cost, along with failure scenarios and recovery strategies. Content is grounded in BigTech practices, including incident post-mortems and lessons from operating systems at global scale. You’ll find deep dives into infrastructure behavior, traffic management, autoscaling, and resilience engineering. Instead of simplified guides, the Highload tag delivers practical engineering insights for backend engineers, architects, platform teams, and SREs responsible for building and maintaining systems that must perform reliably under extreme demand.
MoE routing in direct topologies is limited by unpredictable traffic. An analysis of MoX shows how static routing reduces congestion and approaches switch performance. In classic ML clusters, the network is designed for regular collective operations. This works as long as the traffic is predictable. MoE routing breaks this assumption. Each token selects top-K experts, … Read more
A tight lower bound is established for the round complexity of Byzantine Agreement. An analysis of the impact of an adaptive adversary on latency and consensus architecture.
LLM serving workload turns out to be significantly more complex than is typically modeled. FineServe demonstrates how real workloads break simplified assumptions and impact architecture. Modern LLM platforms operate as always-on services with strict requirements for latency and throughput. The main issue is the unstable and heterogeneous LLM serving workload. Most studies have relied on … Read more
In-band SDN with multi-controller coordination: how Periplus limits forwarding state using border-switch graphs and scales the network without increasing load
AI accelerators for scientific computing: bridging the precision, memory, and execution gap when migrating HPC workloads to NPUs –>
Primitive-level synchronization in distributed PBNR training: how to eliminate global barriers and accelerate training without sacrificing quality
Compute-communication overlap in MoE reduces latency through tiled scheduling and signaling. This directly impacts throughput and GPU utilization. Modern Mixture-of-Experts (MoE) systems are limited not by compute, but by communication. In distributed execution, each layer requires two all-to-all operations, and the second—returning results—falls into the critical path. The classical scheme only initiates it after the … Read more
Asymmetric io_uring in Seastar: how offloading I/O to dedicated cores affects latency and throughput and where bottlenecks arise
Distributed model counting with work-stealing: how gDMC reduces overhead and solves the load balancing problem in #SAT systems
MEV in DAG BFT: how Mysticeti creates transaction ordering bias and why DAG linearization becomes a critical architectural bottleneck
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