tQUAD - Memory Bandwidth Usage Analysis

Ostadzadeh, S. Arash; Corina, Marco; Galuzzi, Carlo; Bertels, Koen

doi:10.1109/icppw.2010.39

Cited by 4 publications

(3 citation statements)

References 20 publications

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“…The QUAD toolset [8] provides a comprehensive overview of the data communication behavior of an application. QUAD measures the actual data transferred between producer function and consumer function.…”

Section: B Profiling Toolsmentioning

confidence: 99%

Rule-based data communication optimization using quantitative communication profiling

Pham‐Quoc

Al-Ars

Bertels

2012

2012 International Conference on Field-Programmable Technology

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Multicore architectures, especially hardware accelerator systems with heterogeneous processing elements, are being increasingly used due to the increasing processing demand of modern digital systems. However, data communication in multicore architectures is one of the main performance bottlenecks. Therefore, reducing data communication overhead is an important method to improve the speed-up of such systems. In this paper, we propose a heuristic-based approach to address the data communication bottleneck. The proposed approach uses a detailed quantitative data communication profiling to generate interconnect designs automatically that are relatively simple, low overhead and low area solutions. Experimental results show that we can gain speed-up of 3.05× for the whole application and up to 7.8× speed-up for accelerator functions in comparison with software.

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Section: B Profiling Toolsmentioning

confidence: 99%

Rule-based data communication optimization using quantitative communication profiling

Pham‐Quoc

Al-Ars

Bertels

2012

2012 International Conference on Field-Programmable Technology

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“…It focuses on four main steps within the entire heterogeneous system design. The first step is related to application profiling [7], [8], [9] and coarse-grained task partitioning [10], [11], which is also the focus of this work. In the second step, finegrained hardware code segmentation is investigated along with possible parallelization of the code segments [12].…”

Section: Research Contextmentioning

confidence: 99%

“…XQUAD [17] augments the memory access profiling data with extra information regarding individual data objects defined in the application source code. TQUAD [8] reveals the memory bandwidth usage of each function in terms of relative execution timings. All the tools in the QUAD toolset are implemented utilizing the Pin [18] Dynamic Binary Instrumentation (DBI) framework.…”

Section: B Quad Dynamic Memory Profiling Toolsetmentioning

confidence: 99%

Profile-guided application partitioning for heterogeneous reconfigurable platforms

Ostadzadeh

Meeuws

Ashraf

et al. 2012

The 16th CSI International Symposium on Computer Architecture and Digital Systems (CADS 2012)

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Abstract-The increased complexity of programming heterogeneous reconfigurable platforms requires a thorough understanding of application behavior, for which developers need sophisticated analysis tools. One particular problem, which severely limits the performance gain of running applications on these platforms, is the inappropriateness of the kernels mapped onto the reconfigurable fabrics. Efficient porting of legacy applications to these emerging heterogeneous platforms demands code tuning considering several critical points, such as, proper kernel size and small memory communication overhead. Detailed profiling information is thus vital for an efficient HW/SW co-design. To facilitate addressing these issues, we developed the Q 2 profiling framework. It consists of two parts: an advanced memory access profiling toolset that provides detailed information on the run-time memory access patterns of an application and a statistical modeling framework that makes predictions for resources, early in the design phase, based on software metrics. The code optimizations triggered by careful analysis of the profiling information is used to tailor existing applications for heterogeneous reconfigurable platforms. In this paper, we examine a real application in detail to show the potential of the proposed profiling framework. Experimental results show that a speedup of 1.3× is achieved by accelerating a merged kernel of four critical functions in the application.

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Runtime extraction of memory access information from the application source code

Ostadzadeh

Corina

Galuzzi

et al. 2011

2011 International Conference on High Performance Computing &Amp; Simulation

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tQUAD - Memory Bandwidth Usage Analysis

Cited by 4 publications

References 20 publications

Rule-based data communication optimization using quantitative communication profiling

Rule-based data communication optimization using quantitative communication profiling

Profile-guided application partitioning for heterogeneous reconfigurable platforms

Runtime extraction of memory access information from the application source code

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