Microprocessors and memory systems su er from a growing gap in performance. We i n troduce Active Pages, a computation model which addresses this gap by shifting data-intensive computations to the memory system. An Active P age consists of a page of data and a set of associated functions which c a n operate upon that data. We describe an implementation of Active P ages on RADram (Recon gurable Architecture DRAM), a memory system based upon the integration of DRAM and recon gurable logic. Results from the SimpleScalar simulator BA97] demonstrate up to 1000X speedups on several applications using the RADram system versus conventional memory systems. We also explore the sensitivity of our results to implementations in other memory technologies.

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High assurance systems used in avionics, medical implants, and cryptographic devices often rely on a small trusted base of hardware and software to manage the rest of the system. Crafting the core of such a system in a way that achieves flexibility, security, and performance requires a careful balancing act. Simple static primitives with hard partitions of space and time are easier to analyze formally, but strict approaches to the problem at the hardware level have been extremely restrictive, failing to allow even the simplest of dynamic behaviors to be expressed.Our approach to this problem is to construct a minimal but configurable architectural skeleton. This skeleton couples a critical slice of the low level hardware implementation with a microkernel in a way that allows information flow properties of the entire construction to be statically verified all the way down to its gate-level implementation. This strict structure is then made usable by a runtime system that delivers more traditional services (e.g. communication interfaces and long-living contexts) in a way that is decoupled from the information flow properties of the skeleton. To test the viability of this approach we design, test, and statically verify the information-flow security of a hardware/software system complete with support for unbounded operation, inter-process communication, pipelined operation, and I/O with traditional devices. The resulting system is provably sound even when adversaries are allowed to execute arbitrary code on the machine, yet is flexible enough to allow caching, pipelining, and other common case optimizations.

Quantum computing (QC) offers huge promise to accelerate a range of computationally intensive benchmarks. Quantum computing is limited, however, by the challenges of decoherence: i.e., a quantum state can only be maintained for short windows of time before it decoheres. While quantum error correction codes can protect against decoherence, fast execution time is the best defense against decoherence, so efficient architectures and effective scheduling algorithms are necessary. This paper proposes the Multi-SIMD QC architecture and then proposes and evaluates effective schedulers to map benchmark descriptions onto Multi-SIMD architectures. The Multi-SIMD model consists of a small number of SIMD regions, each of which may support operations on up to thousands of qubits per cycle. Efficient Multi-SIMD operation requires efficient scheduling. This work develops schedulers to reduce communication requirements of qubits between operating regions, while also improving parallelism.We find that communication to global memory is a dominant cost in QC. We also note that many quantum benchmarks have long serial operation paths (although each operation may be data parallel). To exploit this characteristic, we introduce Longest-Path-First Scheduling (LPFS) which pins operations to SIMD regions to keep data in-place and reduce communication to memory. The use of small, local scratchpad memories also further reduces communication. Our results show a 3% to 308% improvement for LPFS over conventional scheduling algorithms, and an additional 3% to 64% improvement using scratchpad memories. Our work is the most comprehensive software-to-quantum toolflow published to date, with efficient and practical scheduling techniques that reduce communication and increase parallelism for full-scale quantum code executing up to a trillion quantum gate operations.