Virtual Hierarchies - An Architecture for Building and Maintaining Efficient and Resilient Trust Chains

In Public Key Infrastructure (PKI), the simple, monopolistic CA model works fine until we consider the real world. Then, issues such as scalability and mutually suspicious organizations create the need for a multiplicity of CAs, which immediately introduces the problem of how to organize them to balance resilience to compromise against efficiency of path discovery. However, security has given us tools such as secure coprocessing, secret splitting, secret sharing, and threshold cryptography for securely carrying out computations among multiple trust domains; distributed computing has given us peer-to-peer networking, for creating self-organizing distributed systems. In this paper, we use these latter tools to address the former problem by overlaying a virtual hierarchy on a mesh architecture of peer CAs, and achieving both resilience and efficiency

Jenga: Harnessing Heterogeneous Memories through Reconfigurable Cache Hierarchies

Conventional memory systems are organized as a rigid hierarchy, with multiple levels of progressively larger and slower memories. Hierarchy allows a simple, fixed design to benefit a wide range of applications, because working sets settle at the smallest (and fastest) level they fit in. However, rigid hierarchies also cause significant overheads, because each level adds latency and energy even when it does not capture the working set. In emerging systems with heterogeneous memory technologies such as stacked DRAM, these overheads often limit performance and efficiency. We propose Jenga, a reconfigurable cache hierarchy that avoids these pathologies and approaches the performance of a hierarchy optimized for each application. Jenga monitors application behavior and dynamically builds virtual cache hierarchies out of heterogeneous, distributed cache banks. Jenga uses simple hardware support and a novel software runtime to configure virtual cache hierarchies. On a 36-core CMP with a 1 GB stacked-DRAM cache, Jenga outperforms a combination of state-of-the-art techniques by 10% on average and by up to 36%, and does so while saving energy, improving system-wide energy-delay product by 29% on average and by up to 96%

Creating a Virtual Hierarchy From a Relational Database

In data management and modeling, the value of the hierarchical model is that it does not require expensive JOIN operations at runtime; once the hierarchy is built, the relationships among data are embedded in the tree-like hierarchical structure, and thus querying data could be much faster than using a relational database. Today most data is stored in relational databases, but if the data were stored in hierarchies, what would these hierarchies look like? And more importantly, would this transition lead to a more efficient database? This thesis explores these questions by introducing a set of algorithms to convert a relational schema to a hierarchy, that is, a tree-like structure. We show that our algorithms minimize space cost for creating tree nodes while preserving all the relationships and constraints in the schema. Finally, we evaluate the hierarchies on a native XML DBMS with a set of queries

An alternate proof of Wise's Malnormal Special Quotient Theorem

We give an alternate proof of Wise's Malnormal Special Quotient Theorem (MSQT), avoiding cubical small cancellation theory. We also show how to deduce Wise's Quasiconvex Hierarchy Theorem from the MSQT and theorems of Hsu--Wise and Haglund--Wise.Comment: 42 pages, 10 figures. Version 2 contains minor changes, addressing referee comments. To appear in Forum of Mathematics, P

Hierarchical Parallel Matrix Multiplication on Large-Scale Distributed Memory Platforms

Matrix multiplication is a very important computation kernel both in its own right as a building block of many scientific applications and as a popular representative for other scientific applications. Cannon algorithm which dates back to 1969 was the first efficient algorithm for parallel matrix multiplication providing theoretically optimal communication cost. However this algorithm requires a square number of processors. In the mid 1990s, the SUMMA algorithm was introduced. SUMMA overcomes the shortcomings of Cannon algorithm as it can be used on a non-square number of processors as well. Since then the number of processors in HPC platforms has increased by two orders of magnitude making the contribution of communication in the overall execution time more significant. Therefore, the state of the art parallel matrix multiplication algorithms should be revisited to reduce the communication cost further. This paper introduces a new parallel matrix multiplication algorithm, Hierarchical SUMMA (HSUMMA), which is a redesign of SUMMA. Our algorithm reduces the communication cost of SUMMA by introducing a two-level virtual hierarchy into the two-dimensional arrangement of processors. Experiments on an IBM BlueGene-P demonstrate the reduction of communication cost up to 2.08 times on 2048 cores and up to 5.89 times on 16384 cores.Comment: 9 page

Everything You Always Wanted to Know About JagWorks...But Were Afraid to Ask

This is a presentation by Jana Herrmann, Institutional Repository Administrator, about the JagWorks@USA Institutional Repository. This presentation was part of the material presented at the 2023 USA Libraries Staff Summer Workshop