GNNBench: A Plug-and-Play Deep Learning Benchmarking Platform Focused on System Innovation

The absence of a standardized benchmark for Graph Neural Networks GNNs has led to ignored pitfalls in system design and analysis. Present benchmarks like Graph500 and LDBC must be revised for GNNs as a consequence of variations in computations, storage, and reliance on deep studying frameworks. GNN methods goal to optimize runtime and reminiscence with out altering mannequin semantics. Nonetheless, many need assistance with design flaws and constant evaluations, hindering progress. Greater than manually correcting these flaws is required; a scientific benchmarking platform have to be established to make sure equity and consistency throughout assessments. Such a platform would streamline efforts and promote innovation in GNN methods.

William & Mary researchers have developed GNNBENCH, a flexible platform tailor-made for system innovation in GNNs. It streamlines the trade of tensor information, helps customized courses in System APIs, and seamlessly integrates with frameworks like PyTorch and TensorFlow. By combining a number of GNN methods, GNNBENCH uncovered crucial measurement points, aiming to alleviate researchers from integration complexities and analysis inconsistencies. The platform’s stability, productiveness enhancements, and framework-agnostic nature allow speedy prototyping and truthful comparisons, driving developments in GNN system analysis whereas addressing integration challenges and making certain constant evaluations.

In striving for truthful and productive benchmarking, GNNBENCH addresses key challenges present GNN methods face, aiming to supply secure APIs for seamless integration and correct evaluations. These challenges embody instability as a consequence of various graph codecs and kernel variants throughout totally different methods. PyTorch and TensorFlow plugins current limitations in accepting customized graph objects, whereas GNN operations require extra metadata in system APIs, resulting in inconsistencies. DGL’s framework overhead and sophisticated integration course of additional complicate system integration. Regardless of latest DNN benchmark platforms, GNN benchmarking nonetheless must be explored. PyTorch-Geometric (PyG) faces related plugin limitations. These challenges underscore the necessity for a standardized and extensible benchmarking framework like GNNBENCH.

GNNBENCH introduces a producer-only DLPack protocol, simplifying tensor trade between DL frameworks and third-party libraries. Not like conventional approaches, this protocol allows GNNBENCH to make the most of DL framework tensors with out possession switch, enhancing system flexibility and reusability. Generated integration codes facilitate seamless integration with totally different DL frameworks, selling extensibility. The accompanying domain-specific language (DSL) automates code technology for system integration, providing researchers a streamlined method to prototype and implement kernel fusion or different system improvements. Such mechanisms empower GNNBENCH to adapt to various analysis wants effectively and successfully.

GNNBENCH gives versatile integration with widespread deep studying frameworks like PyTorch, TensorFlow, and MXNet, facilitating seamless platform experimentation. Whereas the first analysis leverages PyTorch, compatibility with TensorFlow, demonstrated notably for GCN, underscores its adaptability to any mainstream DL framework. This adaptability ensures researchers can discover various environments with out constraint, enabling exact comparisons and insights into GNN efficiency. GNNBENCH’s flexibility enhances reproducibility and encourages complete analysis, which is important for advancing GNN analysis in different computational contexts.

In conclusion, GNNBENCH emerges as a pivotal benchmarking platform, fostering productive analysis and truthful evaluations in GNNs. Facilitating seamless integration of assorted GNN methods sheds mild on accuracy points in authentic fashions like TC-GNN and GNNAdvisor. Via its producer-only DLPack protocol and technology of crucial integration code, GNNBENCH allows environment friendly prototyping with minimal framework overhead and reminiscence consumption. Its systematic method goals to rectify measurement pitfalls, promote innovation, and guarantee unbiased evaluations, thereby advancing the sector of GNN analysis.

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