convert_yielded(self.fetch_next_chunk()) while True: chunk = await fetch_future if chunk is None: break self.write(chunk) fetch_future = convert_yielded(self.fetch_next_chunk()) await self.flush() This is get(self): fetch_future = self.fetch_next_chunk() while True: chunk = yield fetch_future if chunk is None: break self.write(chunk) fetch_future = self.fetch_next_chunk() yield self.flush() Looping In native those methods are called, so the values are available during prepare. RequestHandler.data_received(chunk: bytes) → Awaitable[None] | None Implement this method to handle streamed request data. Requires

fetch_next_chunk()) while True: chunk = await fetch_future if chunk is None: break self.write(chunk) fetch_future = convert_yielded(self.fetch_next_chunk()) fetch_future = self.fetch_next_chunk() while True: chunk = yield fetch_future if chunk is None: break self.write(chunk) fetch_future = self.fetch_next_chunk() yield self those methods are called, so the values are available during prepare. RequestHandler.data_received(chunk: bytes [https://docs.python.org/3/library/stdtypes.html#bytes]) → Awaitable [https://docs.python.

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

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