Cloud-Native Parallel Computing

Transform Your Parallel Workloads from Cluster Experiments to Scalable Cloud Systems
Are you running parallel jobs that work on a single cluster but break down in the cloud? Struggling with Kubernetes complexity, unpredictable performance, or rising cloud costs?
Do you understand parallel computing—but feel stuck when trying to scale it across modern cloud platforms?
This book bridges the gap between traditional HPC and real-world cloud-native systems.
What You’ll Master
Cloud-Native Parallel Architecture
Design scalable parallel and distributed workloads for cloud environments
Understand the shift from fixed HPC clusters to elastic infrastructure
Choose the right programming models for your workload
Kubernetes and Containerized HPC
Containerize compute-intensive applications for consistent deployment
Use Kubernetes to run parallel jobs with Jobs, scheduling, and autoscaling
Manage dependencies and environments across hundreds of nodes
Multi-Cloud Deployment
Deploy workloads across Amazon Web Services, Google Cloud Platform, and Microsoft Azure
Build portable systems that avoid vendor lock-in
Select the right compute, storage, and networking options
Performance and Cost Optimization
Tune distributed systems for CPU, memory, GPU, and network efficiency
Reduce cloud costs using autoscaling and spot/preemptible resources
Identify bottlenecks in compute, storage, and communication
Production-Ready Operations
Monitor, debug, and troubleshoot distributed workloads
Implement fault-tolerant systems that survive node failures
Apply security and compliance practices in multi-cloud environments
Why This Book?
Most resources either focus on traditional HPC or basic cloud concepts—but not how they come together in real systems.
This book shows you how everything fits together.
You won’t just learn tools—you’ll understand how to design systems that actually work at scale, based on real-world patterns and deployment experience .
Each chapter includes:
Practical architectures and deployment workflows
Real-world scenarios from production environments
Proven patterns for scaling and reliability
Common mistakes—and how to avoid costly failures
Perfect For
DevOps and platform engineers managing compute-intensive workloads
Data scientists and ML engineers scaling training and pipelines
Systems architects designing distributed infrastructure
HPC practitioners transitioning to cloud-native environments
Prerequisites:
Basic understanding of command-line tools, cloud platforms, and programming concepts