Towards a liquid compiler

Thesis (M.S.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1994.Includes bibliographical references (leaves 41-42).by Stephen Brooks Davis.M.S

Linking Scheme code to data-parallel CUDA-C code

In Compute Unified Device Architecture (CUDA), programmers must manage memory operations, synchronization, and utility functions of Central Processing Unit programs that control and issue data-parallel general purpose programs running on a Graphics Processing Unit (GPU). NVIDIA Corporation developed the CUDA framework to enable and develop data-parallel programs for GPUs to accelerate scientific and engineering applications by providing a language extension of C called CUDA-C. A foreign-function interface comprised of Scheme and CUDA-C constructs extends the Gambit Scheme compiler and enables linking of Scheme and data-parallel CUDA-C code to support high-performance parallel computation with reasonably low overhead in runtime. We provide six test cases — implemented both in Scheme and CUDA-C — in order to evaluate performance of our implementation in Gambit and to show 0–35% overhead in the usual case. Our work enables Scheme programmers to develop expressive programs that control and issue data-parallel programs running on GPUs, while also reducing hands-on memory management

A Parallel Virtual Machine for Efficient Scheme Compilation

Programs compiled by Gambit, our Scheme compiler, achieve performance as much as twice that of the fastest available Scheme compilers. Gambit is easily ported, while retaining its high performance, through the use of a simple virtual machine (PVM). PVM allows a wide variety of machineindependent optimizations and it supports parallel computation based on the future construct. PVM conveys high-level information bidirectionally between the machine-independent front end of the compiler and the machine-dependent back end, making it easy to implement a number of common back end optimizations that are difficult to achieve for other virtual machines. PVM is similar to many real computer architectures and has an option to efficiently gather dynamic measurements of virtual machine usage. These measurements can be used in performance prediction for ports to other architectures as well as design decisions related to proposed optimizations and object representations