Cookies Policy
The website need some cookies and similar means to function. If you permit us, we will use those means to collect data on your visits for aggregated statistics to improve our service. Find out More
Accept Reject
  • Menu
Publications

Publications by HumanISE

2017

The ANTAREX tool flow for monitoring and autotuning energy efficient HPC systems

Authors
Silvano, C; Agosta, G; Barbosa, JG; Bartolini, A; Beccari, AR; Benini, L; Bispo, J; Cardoso, JMP; Cavazzoni, C; Cherubin, S; Cmar, R; Gadioli, D; Manelfi, C; Martinovic, J; Nobre, R; Palermo, G; Palkovic, M; Pinto, P; Rohou, E; Sanna, N; Slaninová, K;

Publication
2017 International Conference on Embedded Computer Systems: Architectures, Modeling, and Simulation, SAMOS 2017, Pythagorion, Greece, July 17-20, 2017

Abstract
Designing and optimizing HPC applications are difficult and complex tasks, which require mastering specialized languages and tools for performance tuning. As this is incompatible with the current trend to open HPC infrastructures to a wider range of users, the availability of more sophisticated programming languages and tools to assist and automate the design stages is crucial to provide smoothly migration paths towards novel heterogeneous HPC platforms. The ANTAREX project intends to address these issues by providing a tool flow, a Domain Specific Launguage and APIs to provide application's adaptivity and to runtime manage and autotune applications for heterogeneous HPC systems. Our DSL provides a separation of concerns, where analysis, runtime adaptivity, performance tuning and energy strategies are specified separately from the application functionalities with the goal to increase productivity, significantly reduce time to solution, while making possible the deployment of substantially improved implementations. This paper presents the ANTAREX tool flow and shows the impact of optimization strategies in the context of one of the ANTAREX use cases related to personalized drug design. We show how simple strategies, not devised by typical compilers, can substantially speedup the execution and reduce energy consumption. © 2017 IEEE.

2017

Embedded Computing for High Performance: Efficient Mapping of Computations Using Customization, Code Transformations and Compilation

Authors
Cardoso, JMP; Coutinho, JGF; Diniz, PC;

Publication
Embedded Computing for High Performance: Efficient Mapping of Computations Using Customization, Code Transformations and Compilation

Abstract
Embedded Computing for High Performance: Design Exploration and Customization Using High-level Compilation and Synthesis Tools provides a set of real-life example implementations that migrate traditional desktop systems to embedded systems. Working with popular hardware, including Xilinx and ARM, the book offers a comprehensive description of techniques for mapping computations expressed in programming languages such as C or MATLAB to high-performance embedded architectures consisting of multiple CPUs, GPUs, and reconfigurable hardware (FPGAs). The authors demonstrate a domain-specific language (LARA) that facilitates retargeting to multiple computing systems using the same source code. In this way, users can decouple original application code from transformed code and enhance productivity and program portability. After reading this book, engineers will understand the processes, methodologies, and best practices needed for the development of applications for high-performance embedded computing systems. Focuses on maximizing performance while managing energy consumption in embedded systems Explains how to retarget code for heterogeneous systems with GPUs and FPGAs Demonstrates a domain-specific language that facilitates migrating and retargeting existing applications to modern systems Includes downloadable slides, tools, and tutorials.

2017

Message from ANDARE'17 general and program chairs

Authors
Bartolini, A; Cardoso, JMP; Silvano, C; Palermo, G; Barbosa, J; Marongiu, A; Mustafa, D; Rohou, E; Mantovani, F; Agosta, G; Martinovic, J; Pingali, K; Slaninová, K; Benini, L; Cytowski, M; Palkovic, M; Gerndt, M; Sanna, N; Diniz, P; Rusitoru, R; Eigenmann, R; Patki, T; Fahringer, T; Rosendard, T;

Publication
ACM International Conference Proceeding Series

Abstract

2017

Targeting heterogeneous computing platforms

Authors
Cardoso, JM; Coutinho, JGF; Diniz, PC;

Publication
Embedded Computing for High Performance

Abstract

2017

Code retargeting for CPU-based platforms

Authors
Cardoso, JM; Coutinho, JGF; Diniz, PC;

Publication
Embedded Computing for High Performance

Abstract

2017

Source code transformations and optimizations

Authors
Cardoso, JM; Coutinho, JGF; Diniz, PC;

Publication
Embedded Computing for High Performance

Abstract

  • 338
  • 662