AI accelerators for scientific computing limits
AI accelerators for scientific computing: bridging the precision, memory, and execution gap when migrating HPC workloads to NPUs –>
Infrastructure
Infrastructure on ThecoreGrid covers the design, operation, and evolution of the foundational systems that power modern software at scale.
We explore compute, networking, and storage layers, along with virtualization, containers, and cloud platforms in highload environments. The focus is on production-grade engineering: reliability, fault tolerance, capacity planning, cost efficiency, and secure system design. Topics include Infrastructure as Code, automation, provisioning, multi-region setups, traffic routing, and failure recovery. We analyze real-world trade-offs and operational challenges, supported by BigTech practices, incident post-mortems, and lessons from large-scale infrastructure failures. You’ll find deep dives into observability, performance tuning, and platform reliability under dynamic workloads. Instead of basic setup guides, the Infrastructure tag delivers practical insights for platform engineers, DevOps teams, SREs, and architects responsible for building and maintaining robust, scalable, and efficient infrastructure systems.
ThecoreGrid Radar: Three Key Technology Trends of the Week
A weekly roundup of the architecture insights and releases we’ve been reading. Infrastructure 🔹 FSZ: Breaking the Prediction-Throughput Trade-off in GPU Lossy Compression A new approach to lossy GPU compression increases throughput while preserving prediction quality. The work shows how compression can become a tool for optimizing compute infrastructure, rather than simply a way to … Read more
Primitive-level synchronization accelerates PBNR
Primitive-level synchronization in distributed PBNR training: how to eliminate global barriers and accelerate training without sacrificing quality
Mixture-of-Experts acceleration through overlap
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
io_uring offloads CPU through asymmetric I/O
Asymmetric io_uring in Seastar: how offloading I/O to dedicated cores affects latency and throughput and where bottlenecks arise
Distributed Model Counting without Bottlenecks
Distributed model counting with work-stealing: how gDMC reduces overhead and solves the load balancing problem in #SAT systems
DNS routing for gRPC eliminates thundering herd
DNS routing for gRPC: how changing the Route 53 policy eliminated the thundering herd and distributed the load across 121 million connections without errors
Kubernetes DRA Enhances Resource Control
Dynamic Resource Allocation Kubernetes: how DRA 1.36 changes resource scheduling, increases utilization, and manageability in clusters –>
Container Patterns as the Foundation of Architecture
Container patterns as the foundation of container orchestration: how coordination and architecture of distributed systems are built without excessive complexity.
Kubernetes sharded watch reduces API load
Server-side sharded list and watch in Kubernetes changes the behavior of controllers. This is an attempt to eliminate the system ceiling when working with high-cardinality resources. When Kubernetes clusters grow to tens of thousands of nodes, controllers hit scalability limits not where one would typically expect. The problem arises at the list/watch interaction level with … Read more