Instruction sets, from families like x86 and ARM, are at the center of many ambitious formal-methods projects. Many verification, synthesis, programming, and debugging tools rely on formal semantics of instruction sets, but different tools can use semantics in rather different ways. The best-known work applying single semantics across diverse tools relies on domain-specific languages like Sail, where the language and its translation tools are specialized to the realm of instruction sets. In the context of the open RISC-V instruction-set family, we decided to explore a different approach, with semantics written in a carefully chosen subset of Haskell. This style does not depend on any new language translators, relying instead on parameterization of semantics over type-class instances. We have used a single core semantics to support testing, interactive proof, and model checking of both software and hardware, demonstrating that monads and the ability to abstract over them using type classes can support pleasant prototyping of ISA semantics.

We describe GraFBoost, a flash-based architecture with hardware acceleration for external analytics of multi-terabyte graphs. We compare the performance of GraFBoost with 1 GB of DRAM against various state-of-the-art graph analytics software including FlashGraph, running on a 32-thread Xeon server with 128 GB of DRAM. We demonstrate that despite the relatively small amount of DRAM, GraFBoost achieves high performance with very large graphs no other system can handle, and rivals the performance of the fastest software platforms on sizes of graphs that existing platforms can handle. Unlike in-memory and semi-external systems, GraFBoost uses a constant amount of memory for all problems, and its performance decreases very slowly as graph sizes increase, allowing GraFBoost to scale to much larger problems than possible with existing systems while using much less resources on a single-node system. The key component of GraFBoost is the sort-reduce accelerator, which implements a novel method to sequentialize fine-grained random accesses to flash storage. The sort-reduce accelerator logs random update requests and then uses hardware-accelerated external sorting with interleaved reduction functions. GraFBoost also stores newly updated vertex values generated in each superstep of the algorithm lazily with the old vertex values to further reduce I/O traffic. We evaluate the performance of GraFBoost for PageRank, breadth-first search and betweenness centrality on our FPGA-based prototype (Xilinx VC707 with 1 GB DRAM and 1 TB flash) and compare it to other graph processing systems including a pure software implementation of GrapFBoost.

Abstract The portability of software has become a major commercial issue in recent times. Such portability does not come easily, as few if any computer languages are really portable in practice. An additional complicating factor, especially in the commercial environment, is that the resulting software must be efficient. This paper describes the design and successful implementation of a 500,000+ line portable FORTRAN 77 package for the processing of seismic data. The package exhibits demonstrably high efficiency on a wide variety of machines from minicomputers to the largest supercomputers. Experiences gained during this exercise throw much light on the integration of the various thought processes which occur during the software engineering cycle, especially the notion of locality.