While increasingly more applications are tempted to manage their data in decentralized systems, such as blockchains or distributed ledgers, the data exchange across multiple, potentially heterogeneous, decentralized systems remains an open problem: State-of-the-art protocols cannot meet one or more of the core requirements, such as atomicity, liveness, and scalability. Specifically, in the field of scientific computing, although a blockchain service was recently developed for scientific computing environments, the data exchanges and transactions among distinct ledgers are not supported. Observing that many modern scientific applications are collaborated on by multiple teams and the increasingly complicated (in-situ) workflows thereof, we argue that there is a pressing need to realize an efficient and scalable protocol for distinct ledgers to exchange data in scientific computing. This paper proposes a topological approach to enabling atomic, nonblocking, and scalable data exchanges among an arbitrary number of scientific ledgers in the context of collaborative scientific computing. Specifically, we construct a topological space formed by these ledgers—abstracting those nodes in a cross-ledger transaction as topological objects such as abstract simplex and simplicial complex. These topological objects, in turn, serve as the building blocks of a topological protocol, namely TopoCommit, under practical assumptions. We implement TopoCommit and integrate it into SciChain, a recently published distributed ledger for tracking scientific data provenance. The extensive evaluation of up to 1,008 nodes and 144 distinct ledgers on CloudLab shows that TopoCommit outperforms state-of-the-art protocols by up to 70×.

Many applications deployed to public clouds are concerned about the confidentiality of their outsourced data, such as financial services and electronic patient records. A plausible solution to this problem is homomorphic encryption (HE), which supports certain algebraic operations directly over the ciphertexts. The downside of HE schemes is their significant, if not prohibitive, performance overhead for data-intensive workloads that are very common for outsourced databases, or database-as-a-serve in cloud computing. The objective of this work is to mitigate the performance overhead incurred by the HE module in outsourced databases. To that end, this paper proposes a radix-based parallel caching optimization for accelerating the performance of homomorphic encryption (HE) of outsourced databases in cloud computing. The key insight of the proposed optimization is caching selected radix-ciphertexts in parallel without violating existing security guarantees of the primitive/base HE scheme. We design the radix HE algorithm and apply it to both batch- and incremental-HE schemes; we demonstrate the security of those radix-based HE schemes by showing that the problem of breaking them can be reduced to the problem of breaking their base HE schemes that are known IND-CPA (i.e. Indistinguishability under Chosen-Plaintext Attack). We implement the radix-based schemes as middleware of a 10-node Cassandra cluster on CloudLab; experiments on six workloads show that the proposed caching can boost state-of-the-art HE schemes, such as Paillier and Symmetria, by up to five orders of magnitude.

Microelectrode arrays (MEAs) are physical devices widely used in various science and engineering fields. One common computational challenge when applying a high-density MEA (i.e., a larger number of wires, more accurate locations of abnormal cells) is how to efficiently compute those resistance values provided the nonlinearity of the system of equations with the unknown resistance values per the Kirchhoff law. This paper proposes an algebraic-topological model for MEAs such that we can identify the intrinsic parallelism that cannot be identified by conventional approaches. We implement a system prototype called Parma based on the proposed topological methodology. Experimental results show that Parma outperforms the state-of-the-practice in time, scalability and memory usage: the computation time is two orders of magnitude faster on up to 1,024 cores with almost linear scalability and the memory is much better utilized with proportionally less warm-up time with respect to the number of concurrent threads.