Biosensors for Bimolecular Computing: a Review and Future Perspectives

Biomolecular computing is the field of engineering where computation, storage, communication, and coding are obtained by exploiting interactions between biomolecules, especially DNA, RNA, and enzymes. They are a promising solution in a long-term vision, bringing huge parallelism and negligible power consumption. Despite significant efforts in taking advantage of the massive computational power of biomolecules, many issues are still open along the way for considering biomolecular circuits as an alternative or a complement to competing with complementary metal-oxide-semiconductor (CMOS) architectures. According to the Von Neumann architecture, computing systems are composed of a central processing unit, a storage unit, and input and output (I/O). I/O operations are crucial to drive and read the computing core and to interface it to other devices. In emerging technologies, the complexity overhead and the bottleneck of I/O systems are usually limiting factors. While computing units and memories based on biomolecular systems have been successfully presented in literature, the published I/O operations are still based on laboratory equipment without a real development of integrated I/O. Biosensors are suitable devices for transducing biomolecular interactions by converting them into electrical signals. In this work, we explore the latest advancements in biomolecular computing, as well as in biosensors, with focus on technology suitable to provide the required and still missing I/O devices. Therefore, our goal is to picture out the present and future perspectives about DNA, RNA, and enzymatic-based computing according to the progression in its I/O technologies, and to understand how the field of biosensors contributes to the research beyond CMOS.

Distributed Computing with Modern Shared Memory

Mihail Igor Zablotchi

In this thesis, we revisit classic problems in shared-memory distributed computing through the lenses of (1) emerging hardware technologies and (2) changing requirements. Our contributions consist, on the one hand, in providing a better understanding of the fundamental benefits and limitations of new technologies, and on the other hand, in introducing novel, efficient tools and systems to ease the task of leveraging new technologies or meeting new requirements. First, we look at Remote Direct Memory Access (RDMA), a networking hardware feature which enables a computer to access the memory of a remote computer without involving the remote CPU. In recent years, the distributed computing community has taken an interest in RDMA due to its ultra-low latency and high throughput and has designed systems that take advantage of these characteristics. However, we argue that the potential of RDMA for distributed computing remains largely untapped. We show that RDMAâs unique semantics enable agreement algorithms which improve on fundamental trade-offs in distributed computing between performance and failure-tolerance. Furthermore, we show the practical applicability of our theoretical results through Mu, a state machine replication system which can replicate requests in under 2 microseconds, and can fail-over in under 1 millisecond when failures occur. Muâs replication and fail-over latencies are at least 61% and 90% lower, respectively, than those of prior work. Second, we focus on persistent memory, a novel class of memory technologies which is only now starting to become available. Persistent memory provides byte-addressable persistent storage with access times comparable to traditional DRAM. Recent work has focused on designing tools for working with persistent memory, but little is known about the fundamental cost of providing consistency in persistent memory. Furthermore, important shared-memory primitives do not yet have efficient persistent implementations. We provide an answer to the former question through a tight bound on the number of persistent fences required to implement a lock-free persistent object. We address the latter problem by presenting a novel efficient multi-word compare-and-swap algorithm for persistent memory. Third and finally, we consider the current exponential increase in the amount of data worldwide. Memory capacity has been on the rise for decades, but remains scarce when compared to the rate of data growth. Given this scarcity and the prevalence of concurrent in-memory processing, the classic problem of concurrent memory reclamation remains highly relevant to this day. Previous work in this area has produced solutions which are either (a) fast but easily disrupted by process delays, or (b) slow but robust to process delays. We combine the best of both worlds in QSense, a memory reclamation algorithm which is fast in the common case when there are no process delays and falls back to a robust reclamation algorithm when process delays prevent the fast path from making progress.

EPFL2020

Distributed Computing with Modern Shared Memory

Mihail Igor Zablotchi

EPFL2020