Debugging is an important step in developing a new code, or porting an old one to a new machine. In this session, we will discuss the debugging of frequently encountered bugs in serial code and debugging of parallel (MPI and threaded) codes using DDT.
Debugging is an important step in developing a new code, or porting an old one to a new machine. In this session, we will discuss the debugging of frequently encountered bugs in serial code and debugging of parallel (MPI and threaded) codes using DDT.
Parallel programming in Python. We will cover subprocess, numexpr, multiprocessing, MPI, and other parallel-enabling python packages.
Format: Virtual
Learn how to fully utilize the power of HPC. Discover proven strategies and tools to efficiently scale up from serial jobs to parallel runs across many compute nodes on Niagara.
Learn the basics of shared memory programming with OpenMP. In particular, we will discuss the OpenMP execution and memory model, performance, reductions and load balancing.
Format: Virtual
An overview of GPUs and their use in supercomputers. This workshop will explain what GPUs are, and cover the basic ideas of GPU use in scientific computing. We will introduce several GPU programming frameworks, and demonstrate how to accelerate a solution of a science problem using a GPU. Python or C++ could be used for the assignment.
Format: Virtual
Parallel programming in Python. We will cover subprocess, numexpr, multiprocessing, MPI, and other parallel-enabling python packages.
Format: Virtual
Learn the basics of Message Passing Interface (MPI) programming. Examples and exercises will be based on parallelization of common scientific computing problems.
Format: Virtual
In this workshop, you will learn advanced MPI techniques such as MPI Datatypes, application topology and MPI-IO in the context of a scientific MPI example.
Parallel programming in Python. We will cover subprocess, numexpr, multiprocessing, MPI, and other parallel-enabling python packages.
Format: In person, but also broadcast and recorded.
An overview of GPUs and their use in supercomputers. This workshop will explain what GPUs are, and cover the basic ideas of GPU use in scientific computing. We will introduce several GPU programming frameworks, and demonstrate how to accelerate a solution of a science problem using a GPU. Python or C++ could be used for the assignment.
Format: In person, but also broadcast.
Learn the basics of Message Passing Interface (MPI) programming. Examples and exercises will be based on parallelization of common scientific computing problems.
Format: Online (Zoom)
In this workshop, spread out over three days within one week, you will learn advanced MPI techniques such as MPI Datatypes, MPI-IO and one-sided communications.
Learn the basics of Message Passing Interface (MPI) programming. Examples and exercises will be based on parallelization of common scientific computing problems.
Parallel programming in Python. We will cover subprocess, numexpr, multiprocessing, MPI, and other parallel-enabling python packages.
Learn the basics of shared memory programming with OpenMP. In particular, we will discuss the OpenMP execution and memory model, performance, reductions and load balancing.
Learn parallel programming in R, with a focus on parallel data analysis.
Debugging and profiling are important steps in developing a new code, or porting an old one to a new machine. In this session, we will discuss the debugging of frequently encountered bugs in serial code with gdb and the debugging of parallel (MPI and threaded) codes.