Recent Submissions

  • Abstraction Layer For Standardizing APIs of Task-Based Engines

    Alomairy, Rabab; Ltaief, Hatem; Abduljabbar, Mustafa; Keyes, David (2019-09-02) [Technical Report]
    We introduce AL4SAN, a lightweight library for abstracting the APIs of task-based runtime engines. AL4SAN unifies the expression of tasks and their data dependencies. It supports various dynamic runtime systems relying on compiler technology and user-defined APIs. It enables an application to employ different runtimes and their respective scheduling components, while providing user obliviousness to the underlying hardware configurations. AL4SAN exposes common front-end APIs and connects to different back end runtimes. Experiments on performance and overhead assessments are reported on various shared- and distributed-memory possibly hardware accelerator-equipped systems. A range of workloads, from compute-bound to memory-bound regimes, are employed as proxies for current scientific applications. The low overhead (less than 10%) achieved using a variety of workloads enables AL4SAN to be deployed for fast development of task-based numerical algorithms. More interestingly, AL4SAN enables runtime interoperability by switching runtimes at runtime. Blending runtime systems permits to achieve a twofold speedup on a task-based generalized symmetric eigenvalue solver, relative to state-of-the-art implementations. The ultimate goal of AL4SAN is not to create a new runtime, but to strengthen co-design of existing runtimes/applications, while facilitating user productivity and code portability. The code of AL4SAN is freely available at, with extensions in progress.
  • Scaling Distributed Machine Learning with In-Network Aggregation

    Sapio, Amedeo; Canini, Marco; Ho, Chen-Yu; Nelson, Jacob; Kalnis,Panos; Kim, Changhoon; Krishnamurthy, Arvind; Moshref, Masoud; Ports, Dan R. K.; Richtárik, Peter (2019-02) [Technical Report]
    Training complex machine learning models in parallel is an increasingly important workload. We accelerate distributed parallel training by designing a communication primitive that uses a programmable switch dataplane to execute a key step of the training process. Our approach, SwitchML, reduces the volume of exchanged data by aggregating the model updates from multiple workers in the network. We co-design the switch processing with the end-host protocols and ML frameworks to provide a robust, efficient solution that speeds up training by up to 300%, and at least by 20% for a number of real-world benchmark models.
  • Exploiting Data Sparsity for Large-Scale Matrix Computations

    Akbudak, Kadir; Ltaief, Hatem; Mikhalev, Aleksandr; Charara, Ali; Keyes, David E. (2018-02-24) [Technical Report]
    Exploiting data sparsity in dense matrices is an algorithmic bridge between architectures that are increasingly memory-austere on a per-core basis and extreme-scale applications. The Hierarchical matrix Computations on Manycore Architectures (HiCMA) library tackles this challenging problem by achieving significant reductions in time to solution and memory footprint, while preserving a specified accuracy requirement of the application. HiCMA provides a high-performance implementation on distributed-memory systems of one of the most widely used matrix factorization in large-scale scientific applications, i.e., the Cholesky factorization. It employs the tile low-rank data format to compress the dense data-sparse off-diagonal tiles of the matrix. It then decomposes the matrix computations into interdependent tasks and relies on the dynamic runtime system StarPU for asynchronous out-of-order scheduling, while allowing high user-productivity. Performance comparisons and memory footprint on matrix dimensions up to eleven million show a performance gain and memory saving of more than an order of magnitude for both metrics on thousands of cores, against state-of-the-art open-source and vendor optimized numerical libraries. This represents an important milestone in enabling large-scale matrix computations toward solving big data problems in geospatial statistics for climate/weather forecasting applications.
  • Batched Tile Low-Rank GEMM on GPUs

    Charara, Ali; Keyes, David E.; Ltaief, Hatem (2018-02) [Technical Report]
    Dense General Matrix-Matrix (GEMM) multiplication is a core operation of the Basic Linear Algebra Subroutines (BLAS) library, and therefore, often resides at the bottom of the traditional software stack for most of the scientific applications. In fact, chip manufacturers give a special attention to the GEMM kernel implementation since this is exactly where most of the high-performance software libraries extract the hardware performance. With the emergence of big data applications involving large data-sparse, hierarchically low-rank matrices, the off-diagonal tiles can be compressed to reduce the algorithmic complexity and the memory footprint. The resulting tile low-rank (TLR) data format is composed of small data structures, which retains the most significant information for each tile. However, to operate on low-rank tiles, a new GEMM operation and its corresponding API have to be designed on GPUs so that it can exploit the data sparsity structure of the matrix while leveraging the underlying TLR compression format. The main idea consists in aggregating all operations onto a single kernel launch to compensate for their low arithmetic intensities and to mitigate the data transfer overhead on GPUs. The new TLR GEMM kernel outperforms the cuBLAS dense batched GEMM by more than an order of magnitude and creates new opportunities for TLR advance algorithms.
  • Borehole Tool for the Comprehensive Characterization of Hydrate-bearing Sediments

    Dai, Sheng; Santamarina, Carlos (Office of Scientific and Technical Information (OSTI), 2018-02-01) [Technical Report]
    Reservoir characterization and simulation require reliable parameters to anticipate hydrate deposits responses and production rates. The acquisition of the required fundamental properties currently relies on wireline logging, pressure core testing, and/or laboratory ob-servations of synthesized specimens, which are challenged by testing capabilities and in-nate sampling disturbances. The project reviews hydrate-bearing sediments, properties, and inherent sampling effects, albeit lessen with the developments in pressure core technology, in order to develop robust correlations with index parameters. The resulting information is incorporated into a tool for optimal field characterization and parameter selection with un-certainty analyses. Ultimately, the project develops a borehole tool for the comprehensive characterization of hydrate-bearing sediments at in situ, with the design recognizing past developments and characterization experience and benefited from the inspiration of nature and sensor miniaturization.
  • Performance Impact of Rank-Reordering on Advanced Polar Decomposition Algorithms

    Esposito, Aniello; Keyes, David E.; Ltaief, Hatem; Sukkari, Dalal (2018) [Technical Report]
    We demonstrate the importance of both MPI rank reordering and choice of processor grid topology in the context of advanced dense linear algebra (DLA) applications for distributed-memory systems. In particular, we focus on the advanced polar decomposition (PD) algorithm, based on the QR-based Dynamically Weighted Halley method (QDWH). The QDWH algorithm may be used as the first computational step toward solving symmetric eigenvalue problems and the singular value decomposition. Sukkari et al. (ACM TOMS, 2017) have shown that QDWH may benefit from rectangular instead of square processor grid topologies, which directly impact the performance of the underlying ScaLAPACK algorithms. In this work, we experiment an extensive combination of grid topologies and rank reorderings for different matrix sizes and number of nodes, and use QDWH as a proxy for advanced compute-bound linear algebra operations, since it is rich in dense linear solvers and factorizations. A performance improvement of up to 54% can be observed for QDWH on 800 nodes of a Cray XC system, thanks to an optimal combination, especially in strong scaling mode of operation, for which communication overheads may become dominant. We perform a thorough application profiling to analyze the impact of reordering and grid topologies on the various linear algebra components of the QDWH algorithm. It turns out that point- to-point communications may be considerably reduced thanks to a judicious choice of grid topology, while properly setting the rank reordering using the features from the cray-mpich library.
  • Ubiquitous Asynchronous Computations for Solving the Acoustic Wave Propagation Equation

    Akbudak, Kadir; Ltaief, Hatem; Etienne, Vincent; Abdelkhalak, Rached; Tonellot, Thierry; Keyes, David E. (2018) [Technical Report]
    This paper designs and implements an ubiquitous asynchronous computational scheme for solving the acoustic wave propagation equation with Absorbing Boundary Conditions (ABCs) in the context of seismic imaging applications. While the Convolutional Perfectly Matched Layer (CPML) is typically used as ABCs in the oil and gas industry, its formulation further stresses memory accesses and decreases the arithmetic intensity at the physical domain boundaries. The challenges with CPML are twofold: (1) the strong, inherent data dependencies imposed on the explicit time stepping scheme render asynchronous time integration cumbersome and (2) the idle time is further exacerbated by the load imbalance introduced among processing units. In fact, the CPML formulation of the ABCs requires expensive synchronization points, which may hinder parallel performance of the overall asynchronous time integration. In particular, when deployed in conjunction with the Multicore-optimized Wavefront Diamond (MWD) tiling approach for the inner domain points, it results into a major performance slow down. To relax CPML’s synchrony and mitigate the resulting load imbalance, we embed CPML’s calculation into MWD’s inner loop and carry on the time integration with fine-grained computations in an asynchronous, holistic way. This comes at the price of storing transient results to alleviate dependencies from critical data hazards, while maintaining the numerical accuracy of the original scheme. Performance results on various x86 architectures demonstrate the superiority of MWD with CPML against the standard spatial blocking. To our knowledge, this is the first practical study, which highlights the consolidation of CPML ABCs with asynchronous temporal blocking stencil computations.
  • HLIBCov: Parallel Hierarchical Matrix Approximation of Large Covariance Matrices and Likelihoods with Applications in Parameter Identification

    Litvinenko, Alexander (2017-09-26) [Technical Report]
    The main goal of this article is to introduce the parallel hierarchical matrix library HLIBpro to the statistical community. We describe the HLIBCov package, which is an extension of the HLIBpro library for approximating large covariance matrices and maximizing likelihood functions. We show that an approximate Cholesky factorization of a dense matrix of size $2M\times 2M$ can be computed on a modern multi-core desktop in few minutes. Further, HLIBCov is used for estimating the unknown parameters such as the covariance length, variance and smoothness parameter of a Mat\'ern covariance function by maximizing the joint Gaussian log-likelihood function. The computational bottleneck here is expensive linear algebra arithmetics due to large and dense covariance matrices. Therefore covariance matrices are approximated in the hierarchical ($\H$-) matrix format with computational cost $\mathcal{O}(k^2n \log^2 n/p)$ and storage $\mathcal{O}(kn \log n)$, where the rank $k$ is a small integer (typically $k<25$), $p$ the number of cores and $n$ the number of locations on a fairly general mesh. We demonstrate a synthetic example, where the true values of known parameters are known. For reproducibility we provide the C++ code, the documentation, and the synthetic data.
  • Low-SNR Capacity of MIMO Optical Intensity Channels

    Chaaban, Anas; Rezki, Zouheir; Alouini, Mohamed-Slim (2017-09-18) [Technical Report]
    The capacity of the multiple-input multiple-output (MIMO) optical intensity channel is studied, under both average and peak intensity constraints. We focus on low SNR, which can be modeled as the scenario where both constraints proportionally vanish, or where the peak constraint is held constant while the average constraint vanishes. A capacity upper bound is derived, and is shown to be tight at low SNR under both scenarios. The capacity achieving input distribution at low SNR is shown to be a maximally-correlated vector-binary input distribution. Consequently, the low-SNR capacity of the channel is characterized. As a byproduct, it is shown that for a channel with peak intensity constraints only, or with peak intensity constraints and individual (per aperture) average intensity constraints, a simple scheme composed of coded on-off keying, spatial repetition, and maximum-ratio combining is optimal at low SNR.
  • Partial inversion of elliptic operator to speed up computation of likelihood in Bayesian inference

    Litvinenko, Alexander (2017-08-09) [Technical Report]
    In this paper, we speed up the solution of inverse problems in Bayesian settings. By computing the likelihood, the most expensive part of the Bayesian formula, one compares the available measurement data with the simulated data. To get simulated data, repeated solution of the forward problem is required. This could be a great challenge. Often, the available measurement is a functional $F(u)$ of the solution $u$ or a small part of $u$. Typical examples of $F(u)$ are the solution in a point, solution on a coarser grid, in a small subdomain, the mean value in a subdomain. It is a waste of computational resources to evaluate, first, the whole solution and then compute a part of it. In this work, we compute the functional $F(u)$ direct, without computing the full inverse operator and without computing the whole solution $u$. The main ingredients of the developed approach are the hierarchical domain decomposition technique, the finite element method and the Schur complements. To speed up computations and to reduce the storage cost, we approximate the forward operator and the Schur complement in the hierarchical matrix format. Applying the hierarchical matrix technique, we reduced the computing cost to $\mathcal{O}(k^2n \log^2 n)$, where $k\ll n$ and $n$ is the number of degrees of freedom. Up to the $\H$-matrix accuracy, the computation of the functional $F(u)$ is exact. To reduce the computational resources further, we can approximate $F(u)$ on, for instance, multiple coarse meshes. The offered method is well suited for solving multiscale problems. A disadvantage of this method is the assumption that one has to have access to the discretisation and to the procedure of assembling the Galerkin matrix.
  • Application of Bayesian Networks for Estimation of Individual Psychological Characteristics

    Litvinenko, Alexander; Litvinenko, Natalya (2017-07-19) [Technical Report]
    In this paper we apply Bayesian networks for developing more accurate final overall estimations of psychological characteristics of an individual, based on psychological test results. Psychological tests which identify how much an individual possesses a certain factor are very popular and quite common in the modern world. We call this value for a given factor -- the final overall estimation. Examples of factors could be stress resistance, the readiness to take a risk, the ability to concentrate on certain complicated work and many others. An accurate qualitative and comprehensive assessment of human potential is one of the most important challenges in any company or collective. The most common way of studying psychological characteristics of each single person is testing. Psychologists and sociologists are constantly working on improvement of the quality of their tests. Despite serious work, done by psychologists, the questions in tests often do not produce enough feedback due to the use of relatively poor estimation systems. The overall estimation is usually based on personal experiences and the subjective perception of a psychologist or a group of psychologists about the investigated psychological personality factors.
  • On the Optimality of Repetition Coding among Rate-1 DC-offset STBCs for MIMO Optical Wireless Communications

    Sapenov, Yerzhan; Chaaban, Anas; Rezki, Zouheir; Alouini, Mohamed-Slim (2017-07-06) [Technical Report]
    In this paper, an optical wireless multiple-input multiple-output communication system employing intensity-modulation direct-detection is considered. The performance of direct current offset space-time block codes (DC-STBC) is studied in terms of pairwise error probability (PEP). It is shown that among the class of DC-STBCs, the worst case PEP corresponding to the minimum distance between two codewords is minimized by repetition coding (RC), under both electrical and optical individual power constraints. It follows that among all DC-STBCs, RC is optimal in terms of worst-case PEP for static channels and also for varying channels under any turbulence statistics. This result agrees with previously published numerical results showing the superiority of RC in such systems. It also agrees with previously published analytic results on this topic under log-normal turbulence and further extends it to arbitrary turbulence statistics. This shows the redundancy of the time-dimension of the DC-STBC in this system. This result is further extended to sum power constraints with static and turbulent channels, where it is also shown that the time dimension is redundant, and the optimal DC-STBC has a spatial beamforming structure. Numerical results are provided to demonstrate the difference in performance for systems with different numbers of receiving apertures and different throughput.
  • On the Fast and Precise Evaluation of the Outage Probability of Diversity Receivers Over Generalized Fading Channels

    Ben Issaid, Chaouki; Alouini, Mohamed-Slim; Tempone, Raul (2017-01) [Technical Report]
  • Appendices for: Improper Signaling in Two-Path Relay Channels

    Gaafar, Mohamed; Amin, Osama; Schaefer, Rafael F.; Alouini, Mohamed-Slim (2016-12-01) [Technical Report]
    This document contains the appendices for the work in “Improper Signaling in Two-Path Relay Channels,” which is submitted to 2017 IEEE International Conference on Communications (ICC) Workshop on Full-Duplex Communications for Future Wireless Networks, Paris, France.
  • Asynchronous Task-Based Polar Decomposition on Manycore Architectures

    Sukkari, Dalal; Ltaief, Hatem; Faverge, Mathieu; Keyes, David E. (2016-10-25) [Technical Report]
    This paper introduces the first asynchronous, task-based implementation of the polar decomposition on manycore architectures. Based on a new formulation of the iterative QR dynamically-weighted Halley algorithm (QDWH) for the calculation of the polar decomposition, the proposed implementation replaces the original and hostile LU factorization for the condition number estimator by the more adequate QR factorization to enable software portability across various architectures. Relying on fine-grained computations, the novel task-based implementation is also capable of taking advantage of the identity structure of the matrix involved during the QDWH iterations, which decreases the overall algorithmic complexity. Furthermore, the artifactual synchronization points have been severely weakened compared to previous implementations, unveiling look-ahead opportunities for better hardware occupancy. The overall QDWH-based polar decomposition can then be represented as a directed acyclic graph (DAG), where nodes represent computational tasks and edges define the inter-task data dependencies. The StarPU dynamic runtime system is employed to traverse the DAG, to track the various data dependencies and to asynchronously schedule the computational tasks on the underlying hardware resources, resulting in an out-of-order task scheduling. Benchmarking experiments show significant improvements against existing state-of-the-art high performance implementations (i.e., Intel MKL and Elemental) for the polar decomposition on latest shared-memory vendors' systems (i.e., Intel Haswell/Broadwell/Knights Landing, NVIDIA K80/P100 GPUs and IBM Power8), while maintaining high numerical accuracy.
  • Efficient Outage Probability Evaluation of Diversity Receivers Over Generalized Gamma Channels

    Ben Issaid, Chaouki; Alouini, Mohamed-Slim; Tempone, Raul (2016-10) [Technical Report]
    In this paper, we are interested in determining the cumulative distribution function of the sum of generalized Gamma in the setting of rare event simulations. To this end, we present an efficient importance sampling estimator. The main result of this work is the bounded relative property of the proposed estimator. This result is used to accurately estimate the outage probability of multibranch maximum ratio combining and equal gain combining diversity receivers over generalized Gamma fading channels. Selected numerical simulations are discussed to show the robustness of our estimator compared to naive Monte Carlo.
  • On the Efficient Simulation of the Distribution of the Sum of Gamma-Gamma Variates with Application to the Outage Probability Evaluation Over Fading Channels

    Ben Issaid, Chaouki; Ben Rached, Nadhir; Kammoun, Abla; Alouini, Mohamed-Slim; Tempone, Raul (2016-06) [Technical Report]
    The Gamma-Gamma distribution has recently emerged in a number of applications ranging from modeling scattering and reverbation in sonar and radar systems to modeling atmospheric turbulence in wireless optical channels. In this respect, assessing the outage probability achieved by some diversity techniques over this kind of channels is of major practical importance. In many circumstances, this is intimately related to the difficult question of analyzing the statistics of a sum of Gamma-Gamma random variables. Answering this question is not a simple matter. This is essentially because outage probabilities encountered in practice are often very small, and hence the use of classical Monte Carlo methods is not a reasonable choice. This lies behind the main motivation of the present work. In particular, this paper proposes a new approach to estimate the left tail of the sum of Gamma-Gamma variates. More specifically, we propose a mean-shift importance sampling scheme that efficiently evaluates the outage probability of L-branch maximum ratio combining diversity receivers over Gamma-Gamma fading channels. The proposed estimator satisfies the well-known bounded relative error criterion, a well-desired property characterizing the robustness of importance sampling schemes, for both identically and non-identically independent distributed cases. We show the accuracy and the efficiency of our approach compared to naive Monte Carlo via some selected numerical simulations.
  • A novel mirror diversity receiver for indoor MIMO visible light

    Park, Ki-Hong; Alheadary, Wael G.; Alouini, Mohamed-Slim (2016-03) [Technical Report]
    In this paper, we propose and study a non-imaging receiver design reducing the correlation of channel matrix for indoor multiple-input multiple-output (MIMO) visible light communication (VLC) systems. Contrary to previous works, our proposed mirror diversity receiver (MDR) not only blocks the reception of light on one specific direction but also improves the channel gain on the other direction by receiving the light reflected by a mirror deployed between the photodetectors. We analyze the channel capacity and optimal height of mirror in terms of maximum channel capacity for a 2 -by-2 MIMO-VLC system in a 2-dimensional geometric model.We prove that this constructive and destructive effects in channel matrix resulting from our proposed MDR are more beneficial to obtain well-conditioned channel matrix which is suitable for implementing spatial-multiplexing MIMO-VLC systems in order to support high data rate.
  • Capacity Bounds for Parallel Optical Wireless Channels

    Chaaban, Anas; Rezki, Zouheir; Alouini, Mohamed-Slim (2016-01) [Technical Report]
    A system consisting of parallel optical wireless channels with a total average intensity constraint is studied. Capacity upper and lower bounds for this system are derived. Under perfect channel-state information at the transmitter (CSIT), the bounds have to be optimized with respect to the power allocation over the parallel channels. The optimization of the lower bound is non-convex, however, the KKT conditions can be used to find a list of possible solutions one of which is optimal. The optimal solution can then be found by an exhaustive search algorithm, which is computationally expensive. To overcome this, we propose low-complexity power allocation algorithms which are nearly optimal. The optimized capacity lower bound nearly coincides with the capacity at high SNR. Without CSIT, our capacity bounds lead to upper and lower bounds on the outage probability. The outage probability bounds meet at high SNR. The system with average and peak intensity constraints is also discussed.
  • Supplementary Appendix for: Constrained Perturbation Regularization Approach for Signal Estimation Using Random Matrix Theory

    Suliman, Mohamed Abdalla Elhag; Ballal, Tarig; Kammoun, Abla; Al-Naffouri, Tareq Y. (2016) [Technical Report]
    In this supplementary appendix we provide proofs and additional simulation results that complement the paper (constrained perturbation regularization approach for signal estimation using random matrix theory).

View more