• Publications
  • Influence
An instrumentation approach for hardware-agnostic software characterization
TLDR
We propose a framework based on the LLVM compiler infrastructure that is capable of analyzing the inherent instruction-level parallelism and memory access patterns in sequential and parallel applications. Expand
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An Instrumentation Approach for Hardware-Agnostic Software Characterization
TLDR
We introduce PISA, a framework based on the LLVM infrastructure that is able to generate such a model for sequential and parallel applications by performing hardware-independent characterization. Expand
  • 15
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Predicting cloud performance for HPC applications before deployment
TLDR
We propose a machine-learning methodology to support the user in the selection of the best cloud configuration to run the target workload before deploying it in the cloud. Expand
  • 9
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Scaling Properties of Parallel Applications to Exascale
TLDR
A detailed profile of exascale applications helps to understand the computation, communication and memory requirements for exascALE systems and provides the insight necessary for fine-tuning the computing architecture. Expand
  • 5
  • 1
Scaling application properties to exascale
TLDR
We propose a methodology for extrapolating application properties at exascale from an analysis of workload sizes feasible on current systems. Expand
  • 5
  • 1
Catch It If You Can: Real-Time Network Anomaly Detection with Low False Alarm Rates
TLDR
We propose a real-time network AD system that reduces the manual workload by coupling 2 learning stages and reduces the human intervention rate by 5x. Expand
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Short and Fat: TCP Performance in CEE Datacenter Networks
TLDR
We evaluate the sensitivity of three widespread TCP versions to PFC, as well as to the more involved Quantized Congestion Notification (QCN). Expand
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Predicting Cloud Performance for HPC Applications: A User-Oriented Approach
TLDR
We present a machine-learning based model capable of predicting performance of HPC applications running in the cloud. Expand
  • 14
Analytic processor model for fast design-space exploration
TLDR
We propose an analytic model that takes as inputs a) a parametric microarchitecture-independent characterization of the target workload, and b) a hardware configuration of the core and the memory hierarchy, and returns as output an estimation of processor-core performance. Expand
  • 13
Analytic Multi-Core Processor Model for Fast Design-Space Exploration
TLDR
We propose an analytic multi-core processor-performance model that takes as inputs a) a parametric microarchitecture-independent characterization of the target workload, and b) a hardware configuration of the core and the memory hierarchy. Expand
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