Privacy regulations are multiplying at the same time the importance of data accelerates, highlighting a fundamental tension between data protection and utility. Data are often distributed and transferred across geographic and administrative domains, subjecting them to multiple jurisdictions. In this complex environment, compliance requires privacy by design and automation in enforcing policies and detecting violations. We propose an architecture implementing these principles and discuss how it supports new connected-vehicle services enabled by processing data in a GDPR-compliant way compliant.

Counterfeiting is a major problem in many industries, causing hundreds of billions of dollars in damages every year. Digital twins further increase a product's value, making it critical to secure the link between the physical and digital world. An entire industry has sprung up to address this problem with a wide variety of product-authentication technologies, or crypto anchors, offering many different trade-offs between supported product surface, security level, price sensitivity, ease of use, etc. Each crypto-anchor vendor provides their own APIs for commissioning, verification and monitoring which makes it hard for application developers to support many of them. This paper introduces a platform that provides a generic model of an object secured by a crypto anchor, supporting any number of product authentication technologies by means of crypto-anchor plugins. Applications programmed against this generic model can immediately interoperate with any of the plugged-in crypto anchors. We present a blockchain architecture for the platform whose decentralization matches the highly differentiated industry structure. We've implemented the system on Hyperledger Fabric.

Simulators help computer architects optimize system designs. The limited performance of simulators even of moderate size and detail makes the approach infeasible for design-space exploration of future exascale systems. Analytic models, in contrast, offer very fast turn-around times. In this paper 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. The processor-performance model considers instruction-level parallelism (ILP) per type, models single instruction, multiple data (SIMD) features, and considers cache and memory-bandwidth contention between cores. We validate our model by comparing its performance estimates with measurements from hardware performance counters on Intel Xeon and ARM Cortex-A15 systems. We estimate multi-core contention with a maximum error of 11.4 percent. The average single-thread error increases from 25 percent for a state-of-the-art simulator to 59 percent for our model, but the correlation is still 0.8, a high relative accuracy, while we achieve a speedup of several orders of magnitude. With a much higher capacity than simulators and more reliable insights than back-of-the-envelope calculations it makes automated design-space exploration of exascale systems possible, which we show using a real-world case study from radio astronomy.

Cloud computing enables end users to execute high-performance computing applications by renting the required computing power. This pay-for-use approach enables small enterprises and startups to run HPC-related businesses with a significant saving in capital investment and a short time to market. When deploying an application in the cloud, the users may a) fail to understand the interactions of the application with the software layers implementing the cloud system, b) be unaware of some hardware details of the cloud system, and c) fail to understand how sharing part of the cloud system with other users might degrade application performance. These misunderstandings may lead the users to select suboptimal cloud configurations in terms of cost or performance. To aid the users in selecting the optimal cloud configuration for their applications, we suggest that the cloud provider generate a prediction model for the provided system. We propose applying machine-learning techniques to generate this prediction model. First, the cloud provider profiles a set of training applications by means of a hardware-independent profiler and then executes these applications on a set of training cloud configurations to collect actual performance values. The prediction model is trained to learn the dependencies of actual performance data on the application profile and cloud configuration parameters. The advantage of using a hardware-independent profiler is that the cloud users and the cloud provider can analyze applications on different machines and interface with the same prediction model. We validate the proposed methodology for a cloud system implemented with OpenStack. We apply the prediction model to the NAS parallel benchmarks. The resulting relative error is below 15% and the Pareto optimal cloud configurations finally found when maximizing application speed and minimizing execution cost on the prediction model are also at most 15% away from the actual optimal solutions.

The design of an energy-efficient memory subsystem is one of the key issues that system architects face today. To achieve this goal, architects usually rely on system simulators and trace-based DRAM power models. However, their long execution time makes the approach infeasible for the design-space exploration of next-generation exascale computing systems. Analytic models, in contrast, are orders of magnitude faster. In this paper, we propose a new analytic memory-scheduler-agnostic power model for DRAM, henceforth referred to as MeSAP. Similarly to state-of-the-art trace-based approaches, our analytic model achieves an average error of 20%, while being an order of magnitude faster. Furthermore, we integrate MeSAP into an analytic performance model of general-purpose processors and show its applicability to the design of a computing system targeting scientific image processing applications.

In this paper, 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. To validate our technique, we compare our performance estimates with measurements on an Intel® Xeon® system. The average error increases from 21% for a state-of-the-art simulator to 25% for our model, but we achieve a speedup of several orders of magnitude. Thus, the model enables fast designspace exploration and represents a first step towards an analytic exascale system model.

The Square Kilometre Array (SKA) will be the largest radio telescope in the world, generating data at Pb/s rates. Real-time processing will require 1018compute operations per second and system operating costs will be dominated by energy consumption. In this paper we explore design options for the aperture array of the first SKA construction phase and provide lower bounds on their power consumption. We analyze the system's components from the antenna front-end to the central signal processor and identify the main power consumers. We compare ASIC-based and FPGA-based data processing pipelines and show that ASICs can lead to 1.6 to 4 times more power efficiency.

The Square Kilometre Array (SKA) is a future radio telescope, currently being designed by the worldwide radio-astronomy community. During the first of two construction phases, more than 250,000 antennas will be deployed, clustered in aperture-array stations. The antennas will generate 2.5 Pb/s of data, which needs to be processed in real time. For the processing stages from A/D conversion to central correlation, we propose an ASIC solution using only three chip architectures. The architecture is scalable - additional chips support additional antennas or beams - and versatile - it can relocate its receiver band within a range of a few MHz up to 4GHz. This flexibility makes it applicable to both SKA phases 1 and 2. The proposed chips implement an antenna and station processor for 289 antennas with a power consumption on the order of 600W and a correlator, including corner turn, for 911 stations on the order of 90 kW.

Power dissipation has become a critical design metric in microprocessor-based system design. In a multi-core system, running multiple applications, power and performance can be dynamically traded off using an integrated power management (PM) unit. This PM unit monitors the performance and power of each core and dynamically adjusts the individual voltages and frequencies in order to maximize system performance under a given power budget (usually set by the operating system). This paper presents a performance and power analysis methodology, featuring a simulation model for multi-core systems that can be easily reconfigured for different scenarios and a PM infrastructure for the exploration and analysis of PM algorithms. Two algorithms have been implemented: one for discrete and one for continuous power modes based on non-linear programming. Extensive experiments are reported, illustrating the effect of power management both at the core and the chip level.

Robust Header Compression (ROHC) provides for more efficient use of radio links for wireless communication in a packet switched network. Due to its potential advantages in the wireless access area and the proliferation of network processors in access infrastructure, there exists a need to understand the resource requirements and architectural implications of implementing ROHC in this environment. We present an analysis of the primary functional blocks of ROHC and extract the architectural implications on next-generation network processor design for wireless access. The discussion focuses on memory space and bandwidth dimensioning as well as processing resource budgets. We conclude with an examination of resource consumption and potential performance gains achievable by offloading computationally intensive ROHC functions to application specific hardware assists. We explore the design tradeoffs for hardware assists in the form of reconfigurable hardware, Application-Specific Instruction-set Processors (ASIPs), and Application-Specific Integrated Circuits (ASICs).

We propose to arrange a library of tree patterns into a hierarchy by means of identity operations. Compared with current unstructured approaches, our new method reduces the computational complexity of searching a pattern from O(n-p) to only O(d), d /spl les/ p. Furthermore, the organization reveals synergies between patterns for ASIP instruction-set synthesis, data-path sharing, and code generation.