9,866 research outputs found
Learning from the Success of MPI
The Message Passing Interface (MPI) has been extremely successful as a
portable way to program high-performance parallel computers. This success has
occurred in spite of the view of many that message passing is difficult and
that other approaches, including automatic parallelization and directive-based
parallelism, are easier to use. This paper argues that MPI has succeeded
because it addresses all of the important issues in providing a parallel
programming model.Comment: 12 pages, 1 figur
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A performance comparison of several superscalar processsor [sic] models with a VLIW processor
Superscalar and VLIW processors can both execute multiple instructions each cycle. Each employs a different instruction scheduling method to achieve multiple instruction execution. Superscalar processors schedule instructions dynamically, and VLIW processors execute statically scheduled instructions. This paper quantitatively compares various superscalar processor architectures with a Very Long Instruction Word architecture developed at the University of California, Irvine. An architectural overview and performance analysis of the superscalar processor models and VIPER, a VLIW processor designed to take advantage of the parallelizing capabilities of Percolation Scheduling, are presented. The motivation for this comparison is to study the capability of a dynamically scheduled processor to obtain the same performance achieved by a statically scheduled processor, and examine the hardware resources required by each
On the Resilience of RTL NN Accelerators: Fault Characterization and Mitigation
Machine Learning (ML) is making a strong resurgence in tune with the massive
generation of unstructured data which in turn requires massive computational
resources. Due to the inherently compute- and power-intensive structure of
Neural Networks (NNs), hardware accelerators emerge as a promising solution.
However, with technology node scaling below 10nm, hardware accelerators become
more susceptible to faults, which in turn can impact the NN accuracy. In this
paper, we study the resilience aspects of Register-Transfer Level (RTL) model
of NN accelerators, in particular, fault characterization and mitigation. By
following a High-Level Synthesis (HLS) approach, first, we characterize the
vulnerability of various components of RTL NN. We observed that the severity of
faults depends on both i) application-level specifications, i.e., NN data
(inputs, weights, or intermediate), NN layers, and NN activation functions, and
ii) architectural-level specifications, i.e., data representation model and the
parallelism degree of the underlying accelerator. Second, motivated by
characterization results, we present a low-overhead fault mitigation technique
that can efficiently correct bit flips, by 47.3% better than state-of-the-art
methods.Comment: 8 pages, 6 figure
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