Safe and Efficient Deployment of Data-Parallelisable Applications on Many-Core Platforms: Theory and Practice
Related publications (32)
Graph Chatbot
Chat with Graph Search
Ask any question about EPFL courses, lectures, exercises, research, news, etc. or try the example questions below.
DISCLAIMER: The Graph Chatbot is not programmed to provide explicit or categorical answers to your questions. Rather, it transforms your questions into API requests that are distributed across the various IT services officially administered by EPFL. Its purpose is solely to collect and recommend relevant references to content that you can explore to help you answer your questions.
The shift of commodity hardware from single- to multi-core processors in the early 2000s compelled software developers to take advantage of the available parallelism of multi-cores. Unfortunately, only few---so-called embarrassingly parallel---applications ...
The traditional architecture used by implementations of Replicated State Machines (RSM) does not fully exploit modern multi-core CPUs. This is increasingly the limiting factor in their performance, because network speeds are increasing much faster than the ...
One of the problems proven to be NP-hard in the field of many-core architectures is the partitioning of stream programs. In order to maximize the execution parallelism and obtain the maximal data throughput for a streaming application it is essential to fi ...
The recent proliferation of multi-core processors has moved concurrent programming into mainstream by forcing increasingly more programmers to write parallel code. Using traditional concurrency techniques, such as locking, is notoriously difficult and has ...
The optimal deployment of data streaming applications onto multi-/many-core platform providing real-time guarantees requires to solve the application partitioning and placement, buffer allocation and task mapping and scheduling optimisation problem using t ...
The skyline is an important query operator for multi-criteria decision making. It reduces a dataset to only those points that offer optimal trade-offs of dimensions. In general, it is very expensive to compute. Recently, multicore CPU algorithms have been ...
With increasing complexity and performance demands of emerging compute-intensive data-parallel workloads, many-core computing systems are becoming a popular trend in computer design. Fast and scalable simulation methods are needed to make meaningful predic ...
Fine-grain data parallelism is increasingly common in mainstream processors in the form of long vectors and on-chip GPUs. This paper develops compiler and runtime support to exploit such data parallelism for non-numeric, non-graphic, irregular parallel tas ...
Predicting the scalability of parallel applications is becoming crucial now that the number of cores in modern CPUs doubles roughly every two years. Traditional ways to get some understanding of the scalability of a parallel application rely on extensive e ...
Today, the evolution of software solutions for parallel processing is strong, as a consequence of the mainstream introduction of chip-level multiprocessors. These types of parallel processors, which include general-purpose multi-cores and graphical process ...