Edge computing plays a pivotal role in IoT applications that require rapid and secure data processing. How-ever, these applications are typically resource-demanding, and the resources available at the edge are often s...
详细信息
With the accelerating growth of Big Data, real-world graph processingapplications now need to tackle graphs with billions of vertices and trillions of edges, thereby increasing the demand for effective solutions to a...
详细信息
ISBN:
(纸本)9798350302080
With the accelerating growth of Big Data, real-world graph processingapplications now need to tackle graphs with billions of vertices and trillions of edges, thereby increasing the demand for effective solutions to application scalability. Unfortunately, current approaches to implementing these applications on modern HPC systems exhibit poor scale-out performance with increasing numbers of nodes. The scalability challenges for these applications are driven by large data sizes, synchronization overheads, and fine-grained communications with irregular data accesses and poor locality. This paper presents the scalability of a novel Actor-based programming system, which provides a lightweight runtime that supports fine-grained asynchronous execution and automatic message aggregation atop a Partitioned Global Address Space (PGAS) communication layer. Evaluations of the Jaccard Index and PageRank applications on the NERSC Perlmutter system demonstrate nearly perfect scaling up to 1, 000 nodes and 64K cores (one-third of the targeted 3000-nodes for Perlmutter). In addition, our Actor-based implementations of Jaccard Index and PageRank executed with parallel efficiencies of 85.7% and 63.4% for the largest run of 64K cores. This performance represents a 29.6x speedup relative to UPC and OpenSHMEM versions of PageRank.
Homomorphic encryption (HE) algorithms, particularly the Cheon-Kim-Kim-Song (CKKS) scheme, offer significant potential for secure computation on encrypted data, making them valuable for privacy-preserving machine lear...
详细信息
ISBN:
(纸本)9798350387186;9798350387179
Homomorphic encryption (HE) algorithms, particularly the Cheon-Kim-Kim-Song (CKKS) scheme, offer significant potential for secure computation on encrypted data, making them valuable for privacy-preserving machine learning. However, high latency in large integer operations in the CKKS algorithm hinders the processing of large datasets and complex computations. This paper proposes a novel strategy that combines lossless data compression techniques with the parallelprocessing power of graphics processing units to address these challenges. Our approach demonstrably reduces data size by 90% and achieves significant speedups of up to 100 times compared to conventional approaches. This method ensures data confidentiality while mitigating performance bottlenecks in CKKS-based computations, paving the way for more efficient and scalable HE applications.
We introduce a distributed memory parallel algorithm for force-directed node embedding that places vertices of a graph into a low-dimensional vector space based on the interplay of attraction among neighboring vertice...
详细信息
ISBN:
(纸本)9798350364613;9798350364606
We introduce a distributed memory parallel algorithm for force-directed node embedding that places vertices of a graph into a low-dimensional vector space based on the interplay of attraction among neighboring vertices and repulsion among distant vertices. We develop our algorithms using two sparse matrix operations, SDDMM and SpMM. We propose a configurable pull -push -based communication strategy that optimizes memory usage and data transfers based on computing resources and asynchronous MPI communication to overlap communication and computation. Our algorithm scales up to 256 nodes on distributed supercomputers by surpassing the performance of state-of-the-art algorithms
We present and evaluate TTG, a novel programming model and its C++ implementation that by marrying the ideas of control and data flowgraph programming supports compact specification and efficient distributed execution...
详细信息
ISBN:
(纸本)9781665481069
We present and evaluate TTG, a novel programming model and its C++ implementation that by marrying the ideas of control and data flowgraph programming supports compact specification and efficient distributed execution of dynamic and irregular applications. Programming interfaces that support taskbased execution often only support shared memory parallel environments;a few support distributed memory environments, either by discovering the entire DAG of tasks on all processes, or by introducing explicit communications. The first approach limits scalability, while the second increases the complexity of programming. We demonstrate how TTG can address these issues without sacrificing scalability or programmability by providing higher-level abstractions than conventionally provided by taskcentric programming systems, without impeding the ability of these runtimes to manage task creation and execution as well as data and resource management efficiently. TTG supports distributed memory execution over 2 different task runtimes, PaRSEC and MADNESS. Performance of four paradigmatic applications (in graph analytics, dense and block-sparse linear algebra, and numerical integrodifferential calculus) with various degrees of irregularity implemented in TTG is illustrated on large distributed-memory platforms and compared to the state-of-theart implementations.
We identify the graph data structure, frontiers, operators, an iterative loop structure, and convergence conditions as essential components of graph analytics systems based on the native-graph approach. Using these es...
详细信息
ISBN:
(纸本)9781665497473
We identify the graph data structure, frontiers, operators, an iterative loop structure, and convergence conditions as essential components of graph analytics systems based on the native-graph approach. Using these essential components, we propose an abstraction that captures all the significant programming models within graph analytics, such as bulksynchronous, asynchronous, shared-memory, message-passing, and push vs. pull traversals. Finally, we demonstrate the power of our abstraction with an elegant modern C++ implementation of single-source shortest path and its required components.
SHA-256 plays an important role in widely used applications, such as data security, data integrity, digital signatures, and cryptocurrencies. However, most of the current optimized implementations of SHA-256 are based...
详细信息
Finding patterns in large highly connected datasets is critical for value discovery in business development and scientific research. This work focuses on the problem of subgraph matching on streaming graphs, which pro...
详细信息
ISBN:
(纸本)9781665481069
Finding patterns in large highly connected datasets is critical for value discovery in business development and scientific research. This work focuses on the problem of subgraph matching on streaming graphs, which provides utility in a myriad of real-world applications ranging from social network analysis to cybersecurity. Each application poses a different set of control parameters, including the restrictions for a match, type of data stream, and search granularity. The problem-driven design of existing subgraph matching systems makes them challenging to apply for different problem domains. This paper presents Mnemonic, a programmable system that provides a high-level API and democratizes the development of a wide variety of subgraph matching solutions. Importantly, Mnemonic also delivers key data management capabilities and optimizations to support real-time processing on long-running, high-velocity multi-relational graph streams. The experiments demonstrate the versatility of Mnemonic, as it outperforms several state-of-the-art systems by up to two orders of magnitude.
One of the most important applications of UAVs is person detection for security or rescue tasks. The goal of the proposed paper is to develop, experiment, and compare the performance of two new neural networks based o...
详细信息
ISBN:
(纸本)9798350369458;9798350369441
One of the most important applications of UAVs is person detection for security or rescue tasks. The goal of the proposed paper is to develop, experiment, and compare the performance of two new neural networks based on the transformer architecture, Detection Transformer and Vision Transformer. Two datasets were used, an own one for testing and COCO for learning. The results are promising to take into account the difficulties of person detection at a distance.
Even though actor-based frameworks such as Akka have been used by thousands of companies for over a decade, it is very difficult to configure cloud-based functional actor applications in a way to minimize their respon...
详细信息
暂无评论