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We distill magic states to complete a universal set of fault-tolerant logic gates that is needed for large-scale quantum computing. By encoding better quality input states for our distillation procedure, we can reduce the considerable resource cost of producing magic states. We demonstrate an error-suppressed encoding scheme for a two-qubit input magic state, that we call the CZ state, on an array of superconducting qubits. Using a complete set of projective logical Pauli measurements, that are also tolerant to a single circuit error, we propose a circuit that demonstrates a magic state prepared with infidelity $(1.87 \pm 0.16) \times 10^{-2}$. Additionally, the yield of our scheme increases with the use of adaptive circuit elements that are conditioned in real time on mid-circuit measurement outcomes. We find our results are consistent with variations of the experiment, including where we use only post-selection in place of adaptive circuits, and where we interrogate our output state using quantum state tomography on the data qubits of the code. Remarkably, the error-suppressed preparation experiment demonstrates a fidelity exceeding that of the preparation of the same unencoded magic-state on any single pair of physical qubits on the same device., 10 pages, 7 figures, comments welcome

The mapping of the physical address space to actual physical locations in DRAM is a complex multistage process on today's systems. Research in domains such as operating systems and system security would benefit from proper documentation of that address translation, yet publicly available datasheets are often incomplete. To spare others the effort of reverse-engineering, we present our insights about the address decoding stages of the Intel Xeon E5 v3 and v4 processors in this report, including the layout and the addresses of all involved configuration registers, as far as we have become aware of them in our experiments. In addition, we present a novel technique for reverse-engineering of interleaving functions by mapping physically present DRAM multiple times into the physical address space.

The limited execution speed of current full system simulators restricts their applicability for dynamic analysis to shortrunning workloads. When analyzing memory contents while simulating a kernel build with Simics, we encountered slowdowns of more than 5000x resulting in 10months of total simulation time. Prior work improved the simulation speed by simulating virtual CPU cores on separate physical CPU cores simultaneously or by applying sampling and extrapolation methods to focus costly analyses on short execution windows. However, these approaches inherently su er from limited scalability or trading accuracy for speed. SimuBoost is a novel idea to parallelize functional full system simulation of single-cores. Our approach takes advantage of fast execution through virtualization, taking checkpoints in regular intervals. The parts between subsequent checkpoints are then simulated and analyzed simultaneously in one job per interval. By transferring jobs to multiple nodes, a parallelized and distributed simulation of the target workload can be achieved, thereby e ectively reducing the overall required simulation time. As no implementation of SimuBoost exists yet, we present a formal model to evaluate the general speedup and scalability characteristics of our acceleration technique. We moreover provide a model to estimate the required number of simulation nodes for optimal performance. According to this model, our approach can speed up conventional simulation in a realistic scenario by a factor of 84, while delivering a parallelization efficiency of 94%.

Memory scanning deduplication techniques, as implemented in Linux' Kernel Samepage Merging (KSM), work very well for deduplicating fairly static, anonymous pages with equal content across different virtual machines. However, scanners need very aggressive scan rates when it comes to identifying sharing opportunities with a short life span of up to about 5 min. Otherwise, the scan process is not fast enough to catch those short-lived pages. Our approach generates I/O-based hints in the host to make the memory scanning process more efficient, thus enabling it to find and exploit short-lived sharing opportunities without raising the scan rate. Experiences with similar techniques for paravirtualized guests have shown that pages in a guest’s unified buffer cache are good sharing candidates. We already identify such pages in the host when carrying out I/O-operations on behalf of the guest. The target/source pages in the guest can safely be assumed to be part of the guest’s unified buffer cache. That way, we can determine good sharing hints for the memory scanner. A modification of the guest is not required. We have implemented our approach in Linux. By modifying the KSM scanning mechanism to process these hints preferentially, we move the associated sharing opportunities earlier into the merging stage. Thereby, we deduplicate more pages than the baseline system. In our evaluation, we identify sharing opportunities faster and with less overhead than the traditional linear scanning policy. KSM needs to follow about seven times as many pages as we do, to find a sharing opportunity.