The proliferation of Model Context Protocol (MCP) servers in enterprise infrastructure management has revolutionized AI-driven automation while introducing critical multi-layered security vulnerabilities that traditional cybersecurity frameworks cannot adequately address. This paper presents a comprehensive intelligent guardrail system that addresses the unique security challenges of MCP-driven infrastructure management through a novel four-layer defense architecture. Our solution employs a dedicated guardian LLM that interprets natural language policies and applies contextual reasoning to complex infrastructure scenarios, providing dynamic policy enforcement that adapts to user roles, operational timing, and system context. Unlike existing rule-based security systems, our approach implements guardrails at four distinct control points: input message filtering, tool selection validation, execution-time verification, and post-action auditing. The system addresses critical gaps in existing security solutions by providing infrastructure-specific threat modeling, real-time policy adaptation, and comprehensive audit trails with explainable decision-making through confidence scores and detailed reasoning. Our evaluation demonstrates the system's effectiveness in preventing command injection, privilege escalation, and tool poisoning attacks across various enterprise infrastructure scenarios while maintaining operational agility essential for modern data center management.

Modern heterogeneous supercomputing systems consist of CPUs, GPUs, and high-speed network interconnects. Communication libraries that support efficient inter-process data movement between memory buffers, especially those involving GPU memory, typically require the CPU to orchestrate the data transfer operations. This approach necessitates expensive synchronization between the CPU and GPU, and is ineffective for achieving better compute/communication overlap in applications using techniques like pipelining. A new offload-friendly communication strategy, stream-triggered (ST) communication, is explored to offload the synchronization and data movement operations from the CPU to the GPU. A Message Passing Interface (MPI) one-sided active target synchronization-based implementation is used to illustrate the proposed strategy. A latency-sensitive nearest-neighbor microbenchmark was used to examine various performance characteristics of the implementation. The offloaded implementation showed significant performance improvements both between nodes (inter-node) and within a single node (intranode) when compared to standard MPI active RMA (33% and $\mathbf{2 7 \%}$, respectively) and point-to-point communication ($\mathbf{9 \%}$ and 38%, respectively).

To meet the increasing demands of parallel scientific applications, supercomputers continue to grow in both scale and complexity. The fastest supercomputer in the world, El Capitan, features over a million CPU cores and tens of thousands of GPUs. Applications running on such large-scale systems are particularly susceptible to system noise or interference caused by the operating system (OS) and other services running on the same compute nodes as the application.

In high-performance computing systems, application performance and throughput are dependent on a complex interplay of hardware and software subsystems and variable workloads with competing resource demands. Data-driven insights into the potentially widespread scope and propagationof impact of events, such as faults and contention for shared resources, can be used to drive more effective use of resources, for improved root cause diagnosis, and for predicting performance impacts. We present work developing integrated capabilities for holistic monitoring and analysis to understand and characterize propagation of performance-degrading events. These characterizations can be used to determine and invoke mitigating responses by system administrators, applications, and system software.

This paper describes and evaluates a scalable and efficient resilience scheme based on the concept of containment domains. Containment domains are a programming construct that enable applications to express resilience needs and to interact with the system to tune and specialize error detection, state preservation and restoration, and recovery schemes. Containment domains have weak transactional semantics and are nested to take advantage of the machine and application hierarchies and to enable hierarchical state preservation, restoration, and recovery. We evaluate the scalability and efficiency of containment domains using generalized trace-driven simulation and analytical analysis and show that containment domains are superior to both checkpoint restart and redundant execution approaches.