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Near-Data Processing refers to an architectural hardware and software paradigm, based on the co-location of storage and compute units. Ideally, it will allow to execute application-defined data- or compute-intensive operations in-situ, i.e.…
This paper presents refinements to the execution-cache-memory performance model and a previously published power model for multicore processors. The combination of both enables a very accurate prediction of performance and energy…
With the advent of Grid and application technologies, scientists and engineers are building more and more complex applications to manage and process large data sets, and execute scientific experiments on distributed resources. Such…
For computational fluid dynamics (CFD) applications with a large number of grid points/cells, parallel computing is a common efficient strategy to reduce the computational time. How to achieve the best performance in the modern…
Internet-of-Things end-nodes demand low power processing platforms characterized by heterogeneous dedicated units, controlled by a processor core running concurrent control threads. Such architecture scheme fits one of the main target…
Hybrid MPI+threads programming is gaining prominence, but, in practice, applications perform slower with it compared to the MPI everywhere model. The most critical challenge to the parallel efficiency of MPI+threads applications is slow…
With the current trend of multiprocessor machines towards more and more hierarchical architectures, exploiting the full computational power requires careful distribution of execution threads and data so as to limit expensive remote memory…
Recent advances in computing architectures and networking are bringing parallel computing systems to the masses so increasing the number of potential users of these kinds of systems. In particular, two important technological evolutions are…
This paper proposes a novel model for web crawling suitable for large-scale web data acquisition. This model first divides web data into several sub-data, with each sub-data corresponding to a thread task. In each thread task, web crawling…
Multi-threading allows agents to pursue a heterogeneous collection of tasks in an orderly manner. The view of multi-threading that emerges from thread algebra is applied to the case where a single agent, who may be human, maintains a…
This work describes the design, implementation and performance analysis of a distributed two-tiered storage software. The first tier functions as a distributed software cache implemented using solid-state devices~(NVMes) and the second tier…
A model of sensory information processing is presented. The model assumes that learning of internal (hidden) generative models, which can predict the future and evaluate the precision of that prediction, is of central importance for…
For decades, conventional computers based on the von Neumann architecture have performed computation by repeatedly transferring data between their processing and their memory units, which are physically separated. As computation becomes…
The recent advancements in multicore machines highlight the need to simplify concurrent programming in order to leverage their computational power. One way to achieve this is by designing efficient concurrent data structures (e.g. stacks,…
Traditional von Neumann architecture based processors become inefficient in terms of energy and throughput as they involve separate processing and memory units, also known as~\textit{memory wall}. The memory wall problem is further…
Modern applications process massive data volumes that overwhelm the storage and retrieval capabilities of memory systems, making memory the primary performance and energy-efficiency bottleneck of computing systems. Although many…
Real-time systems, particularly those used in domains like automated driving, are increasingly adopting neural networks. From this trend arises the need for high-performance hardware exhibiting predictable timing behavior. While…
Data-flow is a natural approach to parallelism. However, describing dependencies and control between fine-grained data-flow tasks can be complex and present unwanted overheads. TALM (TALM is an Architecture and Language for Multi-threading)…
Multi-threaded programs have many applications which are widely used such as operating systems. Analyzing multi-threaded programs differs from sequential ones; the main feature is that many threads execute at the same time. The effect of…
We propose DFModel, a modeling framework for mapping dataflow computation graphs onto large-scale systems. Mapping a workload to a system requires optimizing dataflow mappings at various levels, including the inter-chip (between chips)…