Programmers must reason about deadlock, livelock, synchronization, race conditions, shared memory layout, plurality of state, granularity, throughput, latency, memory bottlenecks, etc. Kernel software is where the complexity of parallelism is expressed. (4) Why do you need CUB? Writing, tuning, and maintaining kernel code is perhaps the most challenging, time-consuming aspect of CUDA programming. To reuse shared memory across all three primitives, the thread block statically allocates a union of their TempStorage types. To provide coalesced accesses to device memory, we configure the cub::BlockLoad and cub::BlockStore primitives to access memory using a striped access pattern (where consecutive threads simultaneously access consecutive items) and then transpose the keys into a blocked arrangement of elements across threads. In particular, cub::BlockRadixSort is used to collectively sort the segment of data items that have been partitioned across the thread block. Invoke methods on the primitive instance.Specify communication details (e.g., the TempStorage allocation) to construct an instance of the primitive.Allocate (or alias) an instance of the specialized primitive's nested TempStorage type within a shared memory space.(CUB primitives are also implicitly specialized by the targeted compilation architecture.) Statically specialize the primitive for the specific problem setting at hand, e.g., the data type being sorted, the number of threads per block, the number of keys per thread, optional algorithmic alternatives, etc.The typical usage pattern for a CUB collective is: Because these operations are cooperative, each primitive requires an allocation of shared memory for threads to communicate through. Collectives allow complex parallel code to be re-used rather than re-implemented, and to be re-compiled rather than hand-ported.īlockSortKernel>(d_in, d_out) In this example, threads use cub::BlockLoad, cub::BlockRadixSort, and cub::BlockStore to collectively load, sort and store the block's segment of input items. (2) CUB's collective primitives Collective software primitives are essential for constructing high-performance, maintainable CUDA kernel code.

Compatible with CUDA dynamic parallelism.

Parallel sort, prefix scan, reduction, histogram, etc.

Compatible with arbitrary thread block sizes and types.

Cooperative I/O, sort, scan, reduction, histogram, etc.

Safely specialized for each underlying CUDA architecture.

Cooperative warp-wide prefix scan, reduction, etc.

(1) What is CUB? CUB provides state-of-the-art, reusable software components for every layer of the CUDA programming model:

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