DaCe 0.10

What's New?

• Python frontend improvements: More Python features are supported, such as return values, tuples, and numpy broadcasting. @dace.programs can now call other programs or SDFGs.
• AMD GPU (HIP) Support: AMD GPUs are now fully supported with HIP code generation.
• Easy-to-use transformation APIs: Apply transformation compositions with one call, enumerate subgraph matches manually, and many more functions now available as part of the dace API. See the new tutorial for examples.
• Faster code generation: Backends now generate lower-level code that is more compiler-friendly.
• Instrumentation interface: Setting the instrument property for SDFG nodes and states enables easy-to-use, localized performance reporting with timers, GPU events, and PAPI performance counters.
• DaCe VSCode plugin: Interactive SDFG viewer and optimizer as part of Visual Studio Code. Download the plugin here.
• Type inference and connector types: In addition to automatic type inference, connectors on nodes can now be defined with explicit types, giving more fine-grained control over type reinterpreting and vector types.
• Subgraph transformations: New transformation type that can work on arbitrary subgraphs. For example, fuse any computation within a state with SubgraphFusion.
• Persistent GPU kernel schedule: Launch persistent kernels with a change of a property! Proportion used of GPU multiprocessors is configurable.
• More transformations: Loop manipulation and other new transformations now available with DaCe. Some transformations (such as Vectorization) made more robust to corner cases.
• More tools: Use sdfgcc to quickly compile and optimize .sdfg files from the command line, generating header and library files. Great for interoperability and Makefiles.
• Short DaCe annotation: Data-centric functions can now be annotated with @dace.
• Many minor fixes and additions: More library nodes (such as einsum) and new properties added, enabling faster performance and more productive high-performance coding than ever.