Acceleratororiented

Accelerator-oriented is an approach to design and development that prioritizes hardware accelerators—such as GPUs, TPUs, FPGAs, and ASICs—for core computation. In these practices, accelerators are the primary compute engines, with software and systems arranged to exploit them while managing data movement to and from the host.

The term is used in computer architecture, high-performance computing, and software engineering to describe strategies aimed at maximizing throughput, performance density, or energy efficiency by leveraging specialized hardware.

Techniques include offloading kernels, overlapping computation with data transfer via asynchronous execution, and tiling algorithms to fit accelerator memories. Programming models such as CUDA, SYCL, OpenCL, and FPGA toolchains express acceleration, while compilers optimize vectorization, parallelism, and data locality.

System design emphasizes heterogeneity, with accelerators on-die or in clusters. High-bandwidth interconnects and efficient DMA engines are used to reduce data movement penalties. Hardware-software co-design seeks to balance compute, memory bandwidth, and latency.

Challenges include portability and vendor lock-in, debugging across host and accelerator code, complexity of data movement optimization, and cost and power considerations.

Applications encompass scientific simulation, machine learning training and inference, real-time analytics, and edge AI workloads.

See also: heterogeneous computing, accelerator programming model, GPU computing, FPGA acceleration.