A. Anghel

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

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

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

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

We propose a methodology for extrapolating application properties at exascale from an analysis of workload sizes feasible on current systems. Expand

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

We evaluate the sensitivity of three widespread TCP versions to PFC, as well as to the more involved Quantized Congestion Notification (QCN). Expand

We present a machine-learning based model capable of predicting performance of HPC applications running in the cloud. Expand

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

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