arXiv subjects

Yongchao Liu

Publications and source records attributed to Yongchao Liu.

62 records · Page 4Linked to original sources

arXiv2020

Asymptotic properties of dual averaging algorithm for constrained distributed stochastic optimization

Considering the constrained stochastic optimization problem over a time-varying random network, where the agents are to collectively minimize a sum of objective functions subject to a common constraint set, we investigate asymptotic properties of a distributed algorithm based on dual averaging of gradients. Different from most existing works on distributed dual averaging algorithms that mainly concentrating on their non-asymptotic properties, we not only prove almost sure convergence and the rate of almost sure convergence, but also asymptotic normality and asymptotic efficiency of the algorithm. Firstly, for general constrained convex optimization problem distributed over a random network, we prove that almost sure consensus can be archived and the estimates of agents converge to the same optimal point. For the case of linear constrained convex optimization, we show that the mirror map of the averaged dual sequence identifies the active constraints of the optimal solution with probability 1, which helps us to prove the almost sure convergence rate and then establish asymptotic normality of the algorithm. Furthermore, we also verify that the algorithm is asymptotically optimal. To the best of our knowledge, it seems to be the first asymptotic normality result for constrained distributed optimization algorithms. Finally, a numerical example is provided to justify the theoretical analysis.

math.OC↗

arXiv2020

Woodpecker-DL: Accelerating Deep Neural Networks via Hardware-Aware Multifaceted Optimizations

Accelerating deep model training and inference is crucial in practice. Existing deep learning frameworks usually concentrate on optimizing training speed and pay fewer attentions to inference-specific optimizations. Actually, model inference differs from training in terms of computation, e.g. parameters are refreshed each gradient update step during training, but kept invariant during inference. These special characteristics of model inference open new opportunities for its optimization. In this paper, we propose a hardware-aware optimization framework, namely Woodpecker-DL (WPK), to accelerate inference by taking advantage of multiple joint optimizations from the perspectives of graph optimization, automated searches, domain-specific language (DSL) compiler techniques and system-level exploration. In WPK, we investigated two new automated search approaches based on genetic algorithm and reinforcement learning, respectively, to hunt the best operator code configurations targeting specific hardware. A customized DSL compiler is further attached to these search algorithms to generate efficient codes. To create an optimized inference plan, WPK systematically explores high-speed operator implementations from third-party libraries besides our automatically generated codes and singles out the best implementation per operator for use. Extensive experiments demonstrated that on a Tesla P100 GPU, we can achieve the maximum speedup of 5.40 over cuDNN and 1.63 over TVM on individual convolution operators, and run up to 1.18 times faster than TensorRT for end-to-end model inference.

cs.DC↗

arXiv2019

A Generalized and Robust Method Towards Practical Gaze Estimation on Smart Phone

Gaze estimation for ordinary smart phone, e.g. estimating where the user is looking at on the phone screen, can be applied in various applications. However, the widely used appearance-based CNN methods still have two issues for practical adoption. First, due to the limited dataset, gaze estimation is very likely to suffer from over-fitting, leading to poor accuracy at run time. Second, the current methods are usually not robust, i.e. their prediction results having notable jitters even when the user is performing gaze fixation, which degrades user experience greatly. For the first issue, we propose a new tolerant and talented (TAT) training scheme, which is an iterative random knowledge distillation framework enhanced with cosine similarity pruning and aligned orthogonal initialization. The knowledge distillation is a tolerant teaching process providing diverse and informative supervision. The enhanced pruning and initialization is a talented learning process prompting the network to escape from the local minima and re-born from a better start. For the second issue, we define a new metric to measure the robustness of gaze estimator, and propose an adversarial training based Disturbance with Ordinal loss (DwO) method to improve it. The experimental results show that our TAT method achieves state-of-the-art performance on GazeCapture dataset, and that our DwO method improves the robustness while keeping comparable accuracy.

cs.CV↗

arXiv2017

Parallelized Kendall's Tau Coefficient Computation via SIMD Vectorized Sorting On Many-Integrated-Core Processors

Pairwise association measure is an important operation in data analytics. Kendall's tau coefficient is one widely used correlation coefficient identifying non-linear relationships between ordinal variables. In this paper, we investigated a parallel algorithm accelerating all-pairs Kendall's tau coefficient computation via single instruction multiple data (SIMD) vectorized sorting on Intel Xeon Phis by taking advantage of many processing cores and 512-bit SIMD vector instructions. To facilitate workload balancing and overcome on-chip memory limitation, we proposed a generic framework for symmetric all-pairs computation by building provable bijective functions between job identifier and coordinate space. Performance evaluation demonstrated that our algorithm on one 5110P Phi achieves two orders-of-magnitude speedups over 16-threaded MATLAB and three orders-of-magnitude speedups over sequential R, both running on high-end CPUs. Besides, our algorithm exhibited rather good distributed computing scalability with respect to number of Phis. Source code and datasets are publicly available at http://lightpcc.sourceforge.net.

cs.DC↗

arXiv2016

Parallel Pairwise Correlation Computation On Intel Xeon Phi Clusters

Co-expression network is a critical technique for the identification of inter-gene interactions, which usually relies on all-pairs correlation (or similar measure) computation between gene expression profiles across multiple samples. Pearson's correlation coefficient (PCC) is one widely used technique for gene co-expression network construction. However, all-pairs PCC computation is computationally demanding for large numbers of gene expression profiles, thus motivating our acceleration of its execution using high-performance computing. In this paper, we present LightPCC, the first parallel and distributed all-pairs PCC computation on Intel Xeon Phi (Phi) clusters. It achieves high speed by exploring the SIMD-instruction-level and thread-level parallelism within Phis as well as accelerator-level parallelism among multiple Phis. To facilitate balanced workload distribution, we have proposed a general framework for symmetric all-pairs computation by building bijective functions between job identifier and coordinate space for the first time. We have evaluated LightPCC and compared it to two CPU-based counterparts: a sequential C++ implementation in ALGLIB and an implementation based on a parallel general matrix-matrix multiplication routine in Intel Math Kernel Library (MKL) (all use double precision), using a set of gene expression datasets. Performance evaluation revealed that with one 5110P Phi and 16 Phis, LightPCC runs up to $20.6\times$ and $218.2\times$ faster than ALGLIB, and up to $6.8\times$ and $71.4\times$ faster than single-threaded MKL, respectively. In addition, LightPCC demonstrated good parallel scalability in terms of number of Phis. Source code of LightPCC is publicly available at http://lightpcc.sourceforge.net.

cs.DC↗

arXiv2016

LightScan: Faster Scan Primitive on CUDA Compatible Manycore Processors

Scan (or prefix sum) is a fundamental and widely used primitive in parallel computing. In this paper, we present LightScan, a faster parallel scan primitive for CUDA-enabled GPUs, which investigates a hybrid model combining intra-block computation and inter-block communication to perform a scan. Our algorithm employs warp shuffle functions to implement fast intra-block computation and takes advantage of globally coherent L2 cache and the associated parallel thread execution (PTX) assembly instructions to realize lightweight inter-block communication. Performance evaluation using a single Tesla K40c GPU shows that LightScan outperforms existing GPU algorithms and implementations, and yields a speedup of up to 2.1, 2.4, 1.5 and 1.2 over the leading CUDPP, Thrust, ModernGPU and CUB implementations running on the same GPU, respectively. Furthermore, LightScan runs up to 8.9 and 257.3 times faster than Intel TBB running on 16 CPU cores and an Intel Xeon Phi 5110P coprocessor, respectively. Source code of LightScan is available at http://cupbb.sourceforge.net.

cs.DC↗

arXiv2014

SWAPHI: Smith-Waterman Protein Database Search on Xeon Phi Coprocessors

The maximal sensitivity of the Smith-Waterman (SW) algorithm has enabled its wide use in biological sequence database search. Unfortunately, the high sensitivity comes at the expense of quadratic time complexity, which makes the algorithm computationally demanding for big databases. In this paper, we present SWAPHI, the first parallelized algorithm employing Xeon Phi coprocessors to accelerate SW protein database search. SWAPHI is designed based on the scale-and-vectorize approach, i.e. it boosts alignment speed by effectively utilizing both the coarse-grained parallelism from the many co-processing cores (scale) and the fine-grained parallelism from the 512-bit wide single instruction, multiple data (SIMD) vectors within each core (vectorize). By searching against the large UniProtKB/TrEMBL protein database, SWAPHI achieves a performance of up to 58.8 billion cell updates per second (GCUPS) on one coprocessor and up to 228.4 GCUPS on four coprocessors. Furthermore, it demonstrates good parallel scalability on varying number of coprocessors, and is also superior to both SWIPE on 16 high-end CPU cores and BLAST+ on 8 cores when using four coprocessors, with the maximum speedup of 1.52 and 1.86, respectively. SWAPHI is written in C++ language (with a set of SIMD intrinsics), and is freely available at http://swaphi.sourceforge.net.

cs.DC↗

arXiv2013

High-speed and accurate color-space short-read alignment with CUSHAW2

Summary: We present an extension of CUSHAW2 for fast and accurate alignments of SOLiD color-space short-reads. Our extension introduces a double-seeding approach to improve mapping sensitivity, by combining maximal exact match seeds and variable-length seeds derived from local alignments. We have compared the performance of CUSHAW2 to SHRiMP2 and BFAST by aligning both simulated and real color-space mate-paired reads to the human genome. The results show that CUSHAW2 achieves comparable or better alignment quality compared to SHRiMP2 and BFAST at an order-of-magnitude faster speed and significantly smaller peak resident memory size. Availability: CUSHAW2 and all simulated datasets are available at http://cushaw2.sourceforge.net. Contact: liuy@uni-mainz.de; bertil.schmidt@uni-mainz.de

q-bio.GN↗