TigerGraph’s graph storage engine and processing engine are implemented in C++. Within the family of general purpose procedural languages, C and C++ are considered lower-level compared to other ...

In parallel processing, a software program is written or modified to identify what parts of the computation can be executed on separate processing hardware, Schardl says. Those parts of the ...

Neo4j is an example of a native graph database that was built from the ground up to store pieces of data as nodes and express their connectedness through edges. Xu considers this “Graph 1.0.” The ...

Graph algorithms and processing form the backbone of numerous applications across science and industry, ranging from social network analysis to large-scale data management.

What became apparent in the wake of these graph processing use cases down the scale chain is that for vast amounts of data, ... folks as the comparisons highlight similar benchmarks from other ...

TigerGraph’s eBook “Native Parallel Graphs: The Next Generation of Graph Database for Real-Time Deep Link Analytics,” discusses what developers need to learn in order to leverage the power of graph ...

Graph Mode will enable developers to ingest and schedule neural networks in TensorFlow or ONNX formats. Supported neural networks will include MobileNetV2, ResNet-50 and VGG16.

Flow Computing is making a tough to believe claim: it says it can 100x the performance of any CPU by shifting work to a special parallel processing unit (PPU) inside or outside the chip.