Search Results for "distributed shared memory"

...UCX exposes a set of abstract communication primitives which utilize the best of available hardware resources and offloads. These include RDMA (InfiniBand and RoCE), TCP, GPUs, shared Memory, and network atomic operations. UCX facilitates rapid development by providing a high-level API, masking the low-level details, while maintaining high-performance and scalability. UCX implements best practices for transfer of messages of all sizes, based on accumulated experience gained from applications running on the world’s largest datacenters and supercomputers.

...We provide a sample application where an image function (x,y) -> (R,G,B) is learned. The fully fused MLP component of this framework requires a very large amount of shared memory in its default configuration. It will likely only work on an RTX 3090, an RTX 2080 Ti, or high-end enterprise GPUs. Lower-end cards must reduce the n_neurons parameter or use the CutlassMLP (better compatibility but slower) instead. tiny-cuda-nn comes with a PyTorch extension that allows using the fast MLPs and input encodings from within a Python context. ...

...An extension for OneFlow to target third-party compiler, such as XLA, TensorRT and OpenVINO etc.CUDA runtime is statically linked into OneFlow. OneFlow will work on a minimum supported driver, and any driver beyond. For more information. Distributed performance (efficiency) is the core technical difficulty of the deep learning framework. OneFlow focuses on performance improvement and heterogeneous distributed expansion. It adheres to the core concept and architecture of static compilation and streaming parallelism and solves the memory wall challenge at the cluster level. world-leading level. ...

Channel is a C++ framework for distributed message passing and event dispatching, configurable with its components (msg ids,routing algorithms...) as template parameters. As a namespace shared by peer threads, channel supports scope control and filtering

Photon provides very fast access to data containers (queues, maps, etc.) in shared memory - it can retrieve millions of data records per second. It also uses some RDB concepts like transactions and crash recovery. See web site for details.

STLshm provides an C++ STL Allocator implementation for creating and using STL containers (and any other allocator aware types) in shared memory regions. It supports multiple shared region implementations.

QuickMP (Quick Multi-Processing) is a simple cross-platform C++ API for generating parallel for loops in shared-memory programs, similar to OpenMP. It provides automatic scalable performance based on the number of available processors.

Blackboard implements a lightweight, portable tuple space suitable for multi-agent system and distributed component design. Supports implicit invocation via content-filtered asynchronous events, blocking call semantics, and shared memory messaging.