Comments (1)
Halide was designed with image processing pipelines in mind -- it facilitates powerful structural changes (multithreading, blocking, SIMD, etc.) to achieve excellent performance for these types of methods. Communication patterns during such a computation can be fairly advanced, and Halide is built to support them. It depends on a fairly large compiler framework (LLVM).
Enoki was originally built to parallelize and differentiate code that numerically evaluates Monte Carlo integrals (i.e. lots of independent function evaluations). It is tiny in comparison, and it solves a much more specific problem. Its main purpose is to take a scalar computation and make it "wide" by performing the same computation on many elements of an array (either on the CPU or the GPU). The computation per SIMD lane/GPU thread is generally expected to be independent. The whole process can be differentiated if desired.
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Related Issues (20)
- Force enoki::Array<float, 3> to be 12 bytes HOT 4
- [General question] Figuring out the correct index type for `gather` operations HOT 2
- Double precision for Enoki autodiff (not supported yet) HOT 3
- sign documentation is wrong HOT 1
- Using CUDA backend of Enoki in a multithreaded environment HOT 1
- Difference between this enoki and wjakob/enoki HOT 2
- The behavior of enoki::hsum does not correspond to the documentation HOT 5
- Enoki cannot build on VS2019 16.10 HOT 12
- How to use binary_search overloads in python.
- Python ImportError on Windows with enoki.cuda HOT 2
- build issues: undefined reference to `clock_gettime@GLIBC_2.17'
- AMD GPU code generation? HOT 2
- Runtime dynamic dispatch of functions using Enoki? HOT 1
- SYCL support HOT 1
- Error: zero size memory allocation when calling 'cuda_partition' HOT 1
- Enoki does not generate fma instruction for fmadd with Array<float, 1> and Clang
- using enoki with custom matrix class HOT 1
- simple enoki example does not compile with Intel compiler HOT 1
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- Installation HOT 1
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