The Roofline model graphically represents the attainable upper bound performance of a computer architecture. This paper analyzes the original Roofline model and proposes a novel approach to provide a more insightful performance modeling of modern architectures by introducing cache-awareness, thus significantly improving the guidelines for application optimization. The proposed model was experimentally verified for different architectures by taking advantage of built-in hardware counters with a curve fitness above 90%.

It is unlikely that a hacker is able to compromise sensitive data that is stored in an encrypted form. However, when data is to be processed, it has to be decrypted, becoming vulnerable to attacks. Homomorphic encryption fixes this vulnerability by allowing one to compute directly on encrypted data. In this survey, both previous and current Somewhat Homomorphic Encryption (SHE) schemes are reviewed, and the more powerful and recent Fully Homomorphic Encryption (FHE) schemes are comprehensively studied. The concepts that support these schemes are presented, and their performance and security are analyzed from an engineering standpoint.

Task scheduling is an essential aspect of parallel programming. Most heuristics for this NP-hard problem are based on a simple system model that assumes fully connected processors and concurrent interprocessor communication. Hence, contention for communication resources is not considered in task scheduling, yet it has a strong influence on the execution time of a parallel program. This paper investigates the incorporation of contention awareness into task scheduling. A new system model for task scheduling is proposed, allowing us to capture both end-point and network contention. To achieve this, the communication network is reflected by a topology graph for the representation of arbitrary static and dynamic networks. The contention awareness is accomplished by scheduling the communications, represented by the edges in the task graph, onto the links of the topology graph. Edge scheduling is theoretically analyzed, including aspects like heterogeneity, routing, and causality. The proposed contention-aware scheduling preserves the theoretical basis of task scheduling. It is shown how classic list scheduling is easily extended to this more accurate system model. Experimental results show the significantly improved accuracy and efficiency of the produced schedules.

Unlike usual VLSI approaches necessary for the computation of intensive Low-Density Parity-Check (LDPC) code decoders, this paper presents flexible software-based LDPC decoders. Algorithms and data structures suitable for parallel computing are proposed in this paper to perform LDPC decoding on multicore architectures. To evaluate the efficiency of the proposed parallel algorithms, LDPC decoders were developed on recent multicores, such as off-the-shelf general-purpose x86 processors, Graphics Processing Units (GPUs), and the CELL Broadband Engine (CELL/B.E.). Challenging restrictions, such as memory access conflicts, latency, coalescence, or unknown behavior of thread and block schedulers, were unraveled and worked out. Experimental results for different code lengths show throughputs in the order of 1 ~ 2 Mbps on the general-purpose multicores, and ranging from 40 Mbps on the GPU to nearly 70 Mbps on the CELL/B.E. The analysis of the obtained results allows to conclude that the CELL/B.E. performs better for short to medium length codes, while the GPU achieves superior throughputs with larger codes. They achieve throughputs that in some cases approach very well those obtained with VLSI decoders. From the analysis of the results, we can predict a throughput increase with the rise of the number of cores.

This paper presents a prototype of a platform for biomolecular recognition detection. The system is based on a magnetoresistive biochip that performs biorecognition assays by detecting magnetically tagged targets. All the electronic circuitry for addressing, driving and reading out signals from spin-valve or magnetic tunnel junctions sensors is implemented using off-the-shelf components. Taking advantage of digital signal processing techniques, the acquired signals are processed in real time and transmitted to a digital analyzer that enables the user to control and follow the experiment through a graphical user interface. The developed platform is portable and capable of operating autonomously for nearly eight hours. Experimental results show that the noise level of the described platform is one order of magnitude lower than the one presented by the previously used measurement set-up. Experimental results also show that this device is able to detect magnetic nanoparticles with a diameter of 250 nm at a concentration of about 40 fM. Finally, the biomolecular recognition detection capabilities of the platform are demonstrated by performing a hybridization assay using complementary and non-complementary probes and a magnetically tagged 20mer single stranded DNA target.