High-Performance Computing (HPC) in Cloud Questions and Answers for Viva

Answer-1: HPC refers to the use of supercomputers and parallel processing techniques for solving complex computational problems quickly.

Answer-2: Cloud-based HPC offers on-demand scalability, eliminates the need for upfront infrastructure investment, and provides global access.

Answer-3: Use cases include scientific simulations, genomics, financial modeling, oil and gas exploration, and machine learning.

Answer-4: Parallel computing divides tasks into smaller sub-tasks that are processed simultaneously to improve performance.

Answer-5: Major providers include AWS (with HPC Clusters), Azure (with Azure Batch), and Google Cloud (with HPC VMs).

Answer-6: HPC clusters are groups of interconnected computers (nodes) working together as a single system to perform complex computations.

Answer-7: Job schedulers manage the execution of computational jobs on available resources, ensuring efficiency and fairness.

Answer-8: Message Passing Interface (MPI) is a standard for communication between nodes in a parallel computing environment.

Answer-9: Low latency is critical for high-speed communication between nodes to maintain synchronization and performance.

Answer-10: Workloads that are compute-intensive, time-sensitive, and scalable benefit most from HPC in the cloud.

Answer-11: InfiniBand is a high-speed, low-latency networking technology used in many HPC environments.

Answer-12: Elasticity allows users to scale resources up or down dynamically based on demand, optimizing cost and performance.

Answer-13: A virtual HPC cluster is a collection of cloud-based VMs configured to operate as a traditional HPC system.

Answer-14: Hybrid HPC combines on-premises HPC infrastructure with cloud-based resources to optimize workloads and costs.

Answer-15: Azure CycleCloud is a tool for managing and orchestrating HPC clusters in Azure.

Answer-16: AWS offers EC2 Hpc6id, Hpc6a, and other instance types, FSx for Lustre, and ParallelCluster for HPC workloads.

Answer-17: A head node manages job submissions and cluster resources, acting as the central control unit.

Answer-18: Autoscaling adjusts the number of compute resources automatically based on job demands to optimize performance.

Answer-19: Google offers pre-configured HPC solutions with Compute Engine, Slurm job scheduler, and high-speed networking.

Answer-20: Slurm is an open-source job scheduler used to allocate resources, manage queues, and run jobs on HPC systems.

Answer-21: Options include high-throughput block storage, shared file systems (like Lustre), and object storage.

Answer-22: Lustre is a high-performance parallel file system widely used in HPC for fast data access.

Answer-23: Spot instances are low-cost cloud VMs suitable for fault-tolerant, short-duration HPC tasks.

Answer-24: Concerns include data privacy, secure access, encryption, and compliance with regulatory standards.

Answer-25: Containerization packages HPC applications and dependencies for consistent execution across environments.

Answer-26: Singularity is a container platform designed for HPC environments, preferred for its compatibility and security.

Answer-27: GPUs accelerate parallel processing tasks, especially useful in simulations, ML, and rendering.

Answer-28: Challenges include data transfer latency, cost control, software compatibility, and performance tuning.

Answer-29: Cost estimation considers compute hours, storage, data transfer, licensing, and job duration.

Answer-30: For many use cases, yes; however, some ultra-scale simulations may still require dedicated on-premise systems.

Answer-31: Cloud providers offer monitoring tools like AWS CloudWatch, Azure Monitor, and custom scripts to track performance.

Answer-32: Checkpointing saves the state of a computation, allowing it to resume from that point in case of failure.

Answer-33: Tightly coupled apps require constant communication between nodes, while loosely coupled apps operate more independently.

Answer-34: Elastic Fabric Adapter (EFA) is a network interface for HPC apps requiring high-bandwidth and low-latency communications.

Answer-35: Interconnects enable fast data transmission between nodes and are essential for performance in distributed computing.

Answer-36: Some HPC software licenses are tied to physical infrastructure and require special agreements for cloud use.

Answer-37: Yes, MPI workloads can be executed in the cloud using appropriate VM types and networking configurations.

Answer-38: Burst computing uses cloud resources to handle peak computational demands beyond on-premise capacity.

Answer-39: Data locality refers to placing compute resources close to the data to reduce latency and improve performance.

Answer-40: Preemptible VMs are cost-effective for short, stateless tasks that can tolerate interruptions.

Answer-41: HPC accelerates AI training, large-scale data analysis, and deep learning model optimization.

Answer-42: Certifications like ISO 27001, HIPAA, FedRAMP, and SOC 2 ensure secure HPC operations in regulated industries.

Answer-43: Through redundant infrastructure, checkpointing, autoscaling, and distributed computing.

Answer-44: Consider workload type, resource availability, and cloud provider support (e.g., Slurm, PBS, HTCondor).

Answer-45: Virtualization provides isolated environments for HPC tasks, simplifying resource management and scalability.

Answer-46: Tune networking, choose the right instance types, use parallel file systems, and optimize job scheduling.

Answer-47: It provides scalable, cost-effective, and managed HPC environments without infrastructure overhead.

Answer-48: Orchestration involves automating the deployment and management of HPC clusters and workloads.

Answer-49: Containers package apps; orchestration tools (like Kubernetes) manage deployment, scaling, and execution.

Answer-50: Start with cloud provider tutorials, choose the right instance types, set up a scheduler, and run a test workload.