Top Results for Parallel Computing

Frances "Fran" Allen was an American computer scientist whose pioneering work at IBM established the foundations of compiler optimization and program analysis. Much of her research focused on optimizing compilers for high-performance and parallel computing architectures. In 2006, she became the firs...

CUDA is a parallel computing platform and API developed by NVIDIA. It enables developers to utilize the processing power of NVIDIA’s GPUs for general-purpose computation. This technology accelerates computationally intensive tasks like simulations, data analysis, and machine learning. CUDA is partic...

Managing an HPC cluster involves orchestrating thousands of CPU/GPU cores across specialized hardware using job schedulers like Slurm or LSF. This is far beyond standard cloud compute. It requires expertise in job dependency graphs, resource partitioning, and optimizing code for parallel execution (...

Designing custom digital circuits implemented on Field-Programmable Gate Arrays (FPGAs). This involves writing hardware description languages (VHDL or Verilog) to define logic gates, pipelines, and state machines. It offers performance far exceeding CPUs for specific tasks (like crypto hashing or fi...

Software used to manage and allocate resources across massive clusters of interconnected CPUs/GPUs for scientific simulations. Users submit jobs specifying resource needs (cores, memory, time), and the scheduler manages execution order and failure recovery. This is highly specialized, typically foun...

Michael Scott is a computer scientist and professor at the University of Rochester known for developing the Michael-Scott non-blocking queue algorithm, one of the first practical lock-free concurrent data structures to be widely implemented. This algorithm, published in 1996, has become a fundamenta...

These standards are used to scale computations across hundreds or thousands of CPU cores (clusters). MPI handles message passing between different nodes, while OpenMP handles parallelism within a single node's cores. Mastering this requires rewriting sequential code to explicitly manage data partiti...

Philip Emeagwali was a Nigerian computer scientist whose research fundamentally shaped modern approaches to parallel computing. He developed the “Machine Learning Algorithm” in 1988, a groundbreaking formula that accurately predicted network bandwidth and remains central to understanding distributed...