1,721,047 research outputs found
A GPU-accelerated adaptive FSAI preconditioner for massively parallel simulations
Full text linkThe solution of linear systems of equations is a central task in a number of scientific and engineering applications. In many cases the solution of linear systems may take most of the simulation time thus representing a major bottleneck in the further development of scientific and technical software. For large scale simulations, nowadays accounting for several millions or even billions of unknowns, it is quite common to resort to preconditioned iterative solvers for exploiting their low memory requirements and, at least potential, parallelism. Approximate inverses have been shown to be robust and effective preconditioners in various contexts. In this work, we show how adaptive Factored Sparse Approximate Inverse (aFSAI), characterized by a very high degree of parallelism, can be successfully implemented on a distributed memory computer equipped with GPU accelerators. Taking advantage of GPUs in adaptive FSAI set-up is not a trivial task, nevertheless we show through an extensive numerical experimentation how the proposed approach outperforms more traditional preconditioners and results in a close-to-ideal behavior in challenging linear algebra problems
Statistical analysis of fixed income market
No full textWe present cross and time series analysis of price fluctuations in the US Treasury fixed income market. Bonds have been classified according to a suitable metric based on the correlation among them. The classification shows how the correlation among fixed income securities depends strongly on their maturity. We study also the structure of price fluctuations for single time series
Highly optimized simulations on single- and multi-GPU systems of the 3D Ising spin glass model
No full textOperating system enhancements to prevent the misuse of system calls
No full textWe propose a cost-effective mechanism, to control the invocation of critical, from the security viewpoint, system calls.
The integration into existing UNIX operating systems is carried out by instrumenting the code of the system calls so that the system call itself once invoked checks to see whether the invoking process and the argument values passed comply with the rules held in an access control database.
This method provides simple interception of both system calls and their argument values and do not require changes in the kernel data structures and algorithms. All kernel modifications are transparent to the application processes that can continue to work correctly without needing changes of the source code or re-compilation. A working prototype has been implemented inside the kernel of the Linux operating system, the prototype is able to detect and block also buffer overflow based attacks
The critical-temperature in renormalization-group studies of 1st-order phase-transitions
No full text
Improving password guessing via representation learning
Full text linkLearning useful representations from unstructured data is one of the core challenges, as well as a driving force, of modern data-driven approaches. Deep learning has demonstrated the broad advantages of learning and harnessing such representations.In this paper, we introduce a deep generative model representation learning approach for password guessing. We show that an abstract password representation naturally offers compelling and versatile properties that open new directions in the extensively studied, and yet presently active, password guessing field. These properties can establish novel password generation techniques that are neither feasible nor practical with the existing probabilistic and non-probabilistic approaches. Based on these properties, we introduce: (1) A general framework for conditional password guessing that can generate passwords with arbitrary biases; and (2) an Expectation Maximization-inspired framework that can dynamically adapt the estimated password distribution to match the distribution of the attacked password set
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