2 مستودعات
Mechanisms for establishing execution order and precedence links between hardware-accelerated tasks.
Distinct from GPU Acceleration: Distinct from general GPU acceleration: focuses on the dependency graph logic for hardware tasks rather than the acceleration itself.
Explore 2 awesome GitHub repositories matching operating systems & systems programming · Task Dependencies. Refine with filters or upvote what's useful.
Taskflow is a C++ task-parallel framework designed to build high-performance parallel workflows and complex dependency graphs. It provides a programming model that organizes computational work into directed acyclic graphs, enabling developers to manage concurrency, resource scheduling, and task dependencies across multi-core CPUs and GPU accelerators. The framework distinguishes itself through its ability to orchestrate heterogeneous systems, allowing for the integration of hardware-accelerated kernels and memory operations into unified execution pipelines. It supports dynamic runtime subflow
Establishes execution order between hardware-accelerated tasks by creating precedence links that ensure specific tasks complete before others begin.
oneDNN is a library for deep learning acceleration that provides optimized building blocks for neural network training and inference. It manages tensor computation across CPU and GPU hardware, enabling the execution of high-performance primitives for model training and neural network inference optimization. The project distinguishes itself through hardware-specific kernel optimization and the use of just-in-time compilation to target specific processor instruction sets. It supports quantized neural network execution using both static and dynamic quantization to reduce memory usage and increas
Synchronizes execution between primitives using events or in-order streams to prevent data races in hardware-accelerated tasks.