slower than multiplication. While some arrays — like Array itself — are implemented using a linear chunk of memory and directly use a linear index in their implementations, other arrays — like Diagonal length(a) ÷ Threads.nthreads()) tasks = map(chunks) do chunk Threads.@spawn sum_single(chunk) end chunk_sums = fetch.(tasks) return sum_single(chunk_sums) end sum_multi_good (generic function with 1 Consequently, a good multiprocessing environment should allow control over the "ownership" of a chunk of memory by a particular CPU. Julia provides a multiprocessing environment based on message passing

slower than multiplication. While some arrays — like Array itself — are implemented using a linear chunk of memory and directly use a linear index in their implementations, other arrays — like Diagonal length(a) ÷ Threads.nthreads()) tasks = map(chunks) do chunk Threads.@spawn sum_single(chunk) end chunk_sums = fetch.(tasks) return sum_single(chunk_sums) end sum_multi_good (generic function with 1 Consequently, a good multiprocessing environment should allow control over the "ownership" of a chunk of memory by a particular CPU. Julia provides a multiprocessing environment based on message passing

slower than multiplication. While some arrays — like Array itself — are implemented using a linear chunk of memory and directly use a linear index in their implementations, other arrays — like Diagonal chunks for parallel work. We then use Threads.@spawn to create tasks that individually sum each chunk. Finally, we sum the results from each task using sum_single again: julia> function sum_multi_good(a) length(a) ÷ Threads.nthreads()) tasks = map(chunks) do chunk Threads.@spawn sum_single(chunk) end chunk_sums = fetch.(tasks) return sum_single(chunk_sums) end sum_multi_good (generic function with 1

slower than multiplication. While some arrays — like Array itself — are implemented using a linear chunk of memory and directly use a linear index in their implementations, other arrays — like Diagonal chunks for parallel work. We then use Threads.@spawn to create tasks that individually sum each chunk. Finally, we sum the results from each task using sum_single again: julia> function sum_multi_good(a) length(a) ÷ Threads.nthreads()) tasks = map(chunks) do chunk Threads.@spawn sum_single(chunk) end chunk_sums = fetch.(tasks) return sum_single(chunk_sums) end sum_multi_good (generic function with 1

slower than multiplication. While some arrays — like Array itself — are implemented using a linear chunk of memory and directly use a linear index in their implementations, other arrays — like Diagonal chunks for parallel work. We then use Threads.@spawn to create tasks that individually sum each chunk. Finally, we sum the results from each task using sum_single again: julia> function sum_multi_good(a) length(a) ÷ Threads.nthreads()) tasks = map(chunks) do chunk Threads.@spawn sum_single(chunk) end chunk_sums = fetch.(tasks) return sum_single(chunk_sums) end sum_multi_good (generic function with 1

Vec, } // Rust types and signatures exposed to C++. extern "Rust" { type MultiBuf; fn next_chunk(buf: &mut MultiBuf) -> &[u8]; } // C++ types and signatures exposed to Rust. unsafe extern "C++" mod ffi { // Rust types and signatures exposed to C++. extern "Rust" { type MultiBuf; fn next_chunk(buf: &mut MultiBuf) -> &[u8]; } } (大致) 產生下列 C++： struct MultiBuf final : public ::rust::Opaque noexcept; static ::std::size_t align() noexcept; }; }; ::rust::Slice<::std::uint8_t const> next_chunk(::org::blobstore::MultiBuf &buf) noexcept; 37.2.4 C++ 橋接器宣告 mod ffi { // C++ types and signatures

slower than multiplication. While some arrays — like Array itself — are implemented using a linear chunk of memory and directly use a linear index in their implementations, other arrays — like Diagonal chunks for parallel work. We then use Threads.@spawn to create tasks that individually sum each chunk. Finally, we sum the results from each task using sum_single again: julia> function sum_multi_good(a) cld(length(a), Threads.nthreads())) tasks = map(chunks) do chunk Threads.@spawn sum_single(chunk) end chunk_sums = fetch.(tasks) return sum_single(chunk_sums) end sum_multi_good (generic function with 1

slower than multiplication. While some arrays — like Array itself — are implemented using a linear chunk of memory and directly use a linear index in their implementations, other arrays — like Diagonal chunks for parallel work. We then use Threads.@spawn to create tasks that individually sum each chunk. Finally, we sum the results from each task using sum_single again: julia> function sum_multi_good(a) cld(length(a), Threads.nthreads())) tasks = map(chunks) do chunk Threads.@spawn sum_single(chunk) end chunk_sums = fetch.(tasks) return sum_single(chunk_sums) end sum_multi_good (generic function with 1

slower than multiplication. While some arrays — like Array itself — are implemented using a linear chunk of memory and directly use a linear index in their implementations, other arrays — like Diagonal chunks for parallel work. We then use Threads.@spawn to create tasks that individually sum each chunk. Finally, we sum the results from each task using sum_single again: julia> function sum_multi_good(a) cld(length(a), Threads.nthreads())) tasks = map(chunks) do chunk Threads.@spawn sum_single(chunk) end chunk_sums = fetch.(tasks) return sum_single(chunk_sums) end sum_multi_good (generic function with 1

slower than multiplication. While some arrays — like Array itself — are implemented using a linear chunk of memory and directly use a linear index in their implementations, other arrays — like Diagonal chunks for parallel work. We then use Threads.@spawn to create tasks that individually sum each chunk. Finally, we sum the results from each task using sum_single again: julia> function sum_multi_good(a) cld(length(a), Threads.nthreads())) tasks = map(chunks) do chunk Threads.@spawn sum_single(chunk) end chunk_sums = fetch.(tasks) return sum_single(chunk_sums) end sum_multi_good (generic function with 1