Enabling pulsar and fast transient searches using coherent dedispersion

We present an implementation of the coherent dedispersion algorithm capable of dedispersing high-time-resolution radio observations to many different dispersion measures (DMs). This approach allows the removal of the dispersive effects of the interstellar medium and enables searches for pulsed emission from pulsars and other millisecond-duration transients at low observing frequencies and/or high DMs where time broadening of the signal due to dispersive smearing would otherwise severely reduce the sensitivity. The implementation, called 'cdmt', for Coherent Dispersion Measure Trials, exploits the parallel processing capability of general-purpose graphics processing units to accelerate the computations. We describe the coherent dedispersion algorithm and detail how cdmt implements the algorithm to efficiently compute many coherent DM trials. We present the concept of a semi-coherent dedispersion search, where coherently dedispersed trials at coarsely separated DMs are subsequently incoherently dedispersed at finer steps in DM. The software is used in an ongoing LOFAR pilot survey to test the feasibility of performing semi-coherent dedispersion searches for millisecond pulsars at 135MHz. This pilot survey has led to the discovery of a radio millisecond pulsar -- the first at these low frequencies. This is the first time that such a broad and comprehensive search in DM-space has been done using coherent dedispersion, and we argue that future low-frequency pulsar searches using this approach are both scientifically compelling and feasible. Finally, we compare the performance of cdmt with other available alternatives.Comment: 8 pages, 7 figures, submitted to Astronomy and Computin

Data acquisition system for the MuLan muon lifetime experiment

We describe the data acquisition system for the MuLan muon lifetime experiment at Paul Scherrer Institute. The system was designed to record muon decays at rates up to 1 MHz and acquire data at rates up to 60 MB/sec. The system employed a parallel network of dual-processor machines and repeating acquisition cycles of deadtime-free time segments in order to reach the design goals. The system incorporated a versatile scheme for control and diagnostics and a custom web interface for monitoring experimental conditions.Comment: 19 pages, 8 figures, submitted to Nuclear Instruments and Methods

Performance Modeling and Evaluation of Distributed Deep Learning Frameworks on GPUs

Deep learning frameworks have been widely deployed on GPU servers for deep learning applications in both academia and industry. In training deep neural networks (DNNs), there are many standard processes or algorithms, such as convolution and stochastic gradient descent (SGD), but the running performance of different frameworks might be different even running the same deep model on the same GPU hardware. In this study, we evaluate the running performance of four state-of-the-art distributed deep learning frameworks (i.e., Caffe-MPI, CNTK, MXNet, and TensorFlow) over single-GPU, multi-GPU, and multi-node environments. We first build performance models of standard processes in training DNNs with SGD, and then we benchmark the running performance of these frameworks with three popular convolutional neural networks (i.e., AlexNet, GoogleNet and ResNet-50), after that, we analyze what factors that result in the performance gap among these four frameworks. Through both analytical and experimental analysis, we identify bottlenecks and overheads which could be further optimized. The main contribution is that the proposed performance models and the analysis provide further optimization directions in both algorithmic design and system configuration.Comment: Published at DataCom'201

Security Through Amnesia: A Software-Based Solution to the Cold Boot Attack on Disk Encryption

Disk encryption has become an important security measure for a multitude of clients, including governments, corporations, activists, security-conscious professionals, and privacy-conscious individuals. Unfortunately, recent research has discovered an effective side channel attack against any disk mounted by a running machine\cite{princetonattack}. This attack, known as the cold boot attack, is effective against any mounted volume using state-of-the-art disk encryption, is relatively simple to perform for an attacker with even rudimentary technical knowledge and training, and is applicable to exactly the scenario against which disk encryption is primarily supposed to defend: an adversary with physical access. To our knowledge, no effective software-based countermeasure to this attack supporting multiple encryption keys has yet been articulated in the literature. Moreover, since no proposed solution has been implemented in publicly available software, all general-purpose machines using disk encryption remain vulnerable. We present Loop-Amnesia, a kernel-based disk encryption mechanism implementing a novel technique to eliminate vulnerability to the cold boot attack. We offer theoretical justification of Loop-Amnesia's invulnerability to the attack, verify that our implementation is not vulnerable in practice, and present measurements showing our impact on I/O accesses to the encrypted disk is limited to a slowdown of approximately 2x. Loop-Amnesia is written for x86-64, but our technique is applicable to other register-based architectures. We base our work on loop-AES, a state-of-the-art open source disk encryption package for Linux.Comment: 13 pages, 4 figure