alpaka-group/alpaka

What it solves

It eliminates the need for developers to write separate, vendor-specific code (such as CUDA, HIP, or SYCL) to run parallel kernels on different hardware accelerators. This allows a single implementation of a user kernel to be portable across various CPUs and GPUs without sacrificing performance.

How it works

Alpaka is a header-only C++20 library that abstracts the underlying levels of parallelism. It uses a virtual index domain divided into equal-sized "frames" to traverse data independently of the hardware. Algorithms map these frames and worker threads onto data, executing computations as parallel kernels (SIMT) that can also leverage SIMD units. It supports multiple backends—including CUDA, HIP, SYCL, and OpenMP—which can be used concurrently within a single application to target different hardware vendors.

Who it’s for

It is designed for accelerator developers and researchers creating high-performance applications that need to run efficiently on diverse hardware architectures (x86, ARM, RISC-V, etc.) and various GPU vendors (NVIDIA, AMD, Intel).

Highlights

• Performance Portability: Provides competitive memory-bandwidth performance across both CPU and GPU architectures.
• Multi-Vendor Support: Compatible with NVIDIA, AMD, and Intel GPUs, as well as multiple CPU architectures.
• Flexible Indexing: Kernels are not restricted to three dimensions, unlike native models like CUDA or SYCL.
• Built-in Primitives: Includes helper functions like iota, transform, reduce, and transform-reduce to simplify development.
• Memory Management: Offers host, device, mapped, and managed multi-dimensional views for data operation.