Parallel Computing Practice Exam
Parallel Computing Practice Exam
About Parallel Computing Exam
The Parallel Computing Practice Exam is designed to assess your ability to design, implement, and optimise programs that run on multiple processors and accelerators. This certification measures your understanding of parallel architectures, programming models, performance metrics, and best practices. Whether you are a software engineer, researcher, or student, this exam will help you show your skills in writing fast and scalable code.
Who should take the Exam?
- Software engineers building high-performance applications
- Computer science students learning parallel models
- Researchers working on parallel algorithms
- DevOps and infrastructure engineers managing compute clusters
- Data scientists processing large datasets
- Anyone preparing for interviews in high-performance computing
Skills Required
- Basic programming in C, C++ or Python
- Understanding of algorithms and data structures
- Familiarity with computer architecture concepts
- Ability to compile and run simple code
Knowledge Gained
- Principles of parallel versus sequential computing
- Writing shared-memory code with OpenMP
- Implementing message-passing programs with MPI
- Basics of GPU computing using CUDA or OpenCL
- Designing and analysing parallel algorithms
- Handling synchronization, race conditions, and deadlocks
- Profiling and tuning code for speed and efficiency
- Using hybrid models and working in cluster/cloud environments
Course Outline
Domain 1 – Foundations of Parallel Computing
- Parallel vs sequential execution
- Flynn’s taxonomy (SISD, SIMD, MIMD)
- Speedup, efficiency, and Amdahl’s law
Domain 2 – Shared Memory Programming with OpenMP
- OpenMP directives and pragmas
- Work-sharing constructs
- Critical sections and atomic operations
Domain 3 – Distributed Memory Programming with MPI
- Point-to-point communication
- Collective operations (broadcast, reduce)
- Process groups and topologies
Domain 4 – GPU Computing with CUDA and OpenCL
- GPU hardware basics
- Writing and launching kernels
- Memory management on GPU
Domain 5 – Parallel Algorithms and Patterns
- Data and task parallelism
- Decomposition strategies
- Common patterns: map-reduce, stencil
Domain 6 – Concurrency and Synchronization
- Race conditions and deadlocks
- Mutexes, semaphores, barriers
- Thread safety and lock-free code
Domain 7 – Performance Analysis and Tuning
- Profiling tools and techniques
- Load balancing and scalability testing
- Optimization strategies
Domain 8 – Advanced Topics in Parallel Computing
- Hybrid MPI+OpenMP programming
- Cloud and cluster computing
- Fault tolerance and large-scale scalability
