New Features Cutout: Cutout allows developers to take large DaCe programs and cut out subgraphs reliably to create a runnable sub-program. This sub-program can be then used to check for correctness, benchmark, and transform a part of a program without having to run the full application. * Example usage from Python: def my_method(sdfg: dace.SDFG, state: dace.SDFGState): nodes = [n for n in state if isinstance(n, dace.nodes.LibraryNode)] # Cut every library node cut_sdfg: dace.SDFG = cutout.cutout_state(state, *nodes) # The cut SDFG now includes each library node and all the necessary arrays to call it with Also available in the SDFG editor: <img src="https://user-images.githubusercontent.com/8348955/155983136-01638491-26d1-40f8-82f8-7b149215e3c1.gif" height="200px" /> Data Instrumentation: Just like node instrumentation for performance analysis, data instrumentation allows users to set access nodes to be saved to an instrumented data report, and loaded later for exact reproducible runs. * Data instrumentation natively works with CPU and GPU global memory, so there is no need to copy data back * Combined with Cutout, this is a powerful interface to perform local optimizations in large applications with ease! * Example use: @dace.program def tester(A: dace.float64[20, 20]): tmp = A + 1 return tmp + 5 sdfg = tester.to_sdfg() for node, _ in sdfg.all_nodes_recursive(): # Instrument every access node if isinstance(node, nodes.AccessNode): node.instrument = dace.DataInstrumentationType.Save A = np.random.rand(20, 20) result = sdfg(A) # Get instrumented data from report dreport = sdfg.get_instrumented_data() assert np.allclose(dreport['A'], A) assert np.allclose(dreport['tmp'], A + 1) assert np.allclose(dreport['__return'], A + 6) Logical Groups: SDFG elements can now be grouped by any criteria, and they will be colored during visualization by default (by @phschaad). See example in action: <img src="https://user-images.githubusercontent.com/8348955/155984287-79eaad11-1022-4857-8d79-97cc60855b20.gif" height="200px" /> Changes and Bug Fixes Samples and tutorials have now been updated to reflect the latest API Constants (added with sdfg.add_constant) can now be used as access nodes in SDFGs. The constants are hard-coded into the generated program, so you can run code with the best performance possible. View nodes can now use the views connector to disambiguate which access node is being viewed Python frontend: else clause is now handled in for and while loops Scalars have been removed from the __dace_init generated function signature (by @orausch) Multiple clock signals in the RTL codegen (by @carljohnsen) Various fixes to frontends, transformations, and code generators Full Changelog available at https://github.com/spcl/dace/compare/v0.12...v0.13