Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userAn Efficient Universal Construction for Large Objects
59
0
0.0
(
0
)
Added by
Nikolaos Kallimanis
Publication date
2020
fields
Informatics Engineering
and research's language is
English
Ask ChatGPT about the research
No Arabic abstract
This paper presents L-UC, a universal construction that efficiently implements dynamic objects of large state in a wait-free manner. The step complexity of L-UC is O(n+kw), where n is the number of processes, k is the interval contention (i.e., the maximum number of active processes during the execution interval of an operation), and w is the worst-case time complexity to perform an operation on the sequential implementation of the simulated object. L-UC efficiently implements objects whose size can change dynamically. It improves upon previous universal constructions either by efficiently handling objects whose state is large and can change dynamically, or by achieving better step complexity.
rate research
Read More
Concurrency has been a subject of study for more than 50 years. Still, many developers struggle to adapt their sequential code to be accessed concurrently. This need has pushed for generic solutions and specific concurrent data structures. Wait-free universal constructs are attractive as they can turn a sequential implementation of any object into an equivalent, yet concurrent and wait-free, implementation. While highly relevant from a research perspective, these techniques are of limited practical use when the underlying object or data structure is sizable. The copy operation can consume much of the CPUs resources and significantly degrade performance. To overcome this limitation, we have designed CX, a multi-instance-based wait-free universal construct that substantially reduces the amount of copy operations. The construct maintains a bounded number of instances of the object that can potentially be brought up to date. We applied CX to several sequential implementations of data structures, including STL implementations, and compared them with existing wait-free constructs. Our evaluation shows that CX performs significantly better in most experiments, and can even rival with hand-written lock-free and wait-free data structures, simultaneously providing wait-free progress, safe memory reclamation and high reader scalability.
Stencil computation is one of the most important kernels in various scientific and engineering applications. A variety of work has focused on vectorization and tiling techniques, aiming at exploiting the in-core data parallelism and data locality respectively. In this paper, the downsides of existing vectorization schemes are analyzed. Briefly, they either incur data alignment conflicts or hurt the data locality when integrated with tiling. Then we propose a novel transpose layout to preserve the data locality for tiling and reduce the data reorganization overhead for vectorization simultaneously. To further improve the data reuse at the register level, a time loop unroll-and-jam strategy is designed to perform multistep stencil computation along the time dimension. Experimental results on the AVX-2 and AVX-512 CPUs show that our approach obtains a competitive performance.
We present GraphMatch, an approximate yet efficient method for building the matching graph for large-scale structure-from-motion (SfM) pipelines. Unlike modern SfM pipelines that use vocabulary (Voc.) trees to quickly build the matching graph and avoid a costly brute-force search of matching image pairs, GraphMatch does not require an expensive offline pre-processing phase to construct a Voc. tree. Instead, GraphMatch leverages two priors that can predict which image pairs are likely to match, thereby making the matching process for SfM much more efficient. The first is a score computed from the distance between the Fisher vectors of any two images. The second prior is based on the graph distance between vertices in the underlying matching graph. GraphMatch combines these two priors into an iterative sample-and-propagate scheme similar to the PatchMatch algorithm. Its sampling stage uses Fisher similarity priors to guide the search for matching image pairs, while its propagation stage explores neighbors of matched pairs to find new ones with a high image similarity score. Our experiments show that GraphMatch finds the most image pairs as compared to competing, approximate methods while at the same time being the most efficient.
MLModelCI provides multimedia researchers and developers with a one-stop platform for efficient machine learning (ML) services. The system leverages DevOps techniques to optimize, test, and manage models. It also containerizes and deploys these optimized and validated models as cloud services (MLaaS). In its essence, MLModelCI serves as a housekeeper to help users publish models. The models are first automatically converted to optimized formats for production purpose and then profiled under different settings (e.g., batch size and hardware). The profiling information can be used as guidelines for balancing the trade-off between performance and cost of MLaaS. Finally, the system dockerizes the models for ease of deployment to cloud environments. A key feature of MLModelCI is the implementation of a controller, which allows elastic evaluation which only utilizes idle workers while maintaining online service quality. Our system bridges the gap between current ML training and serving systems and thus free developers from manual and tedious work often associated with service deployment. We release the platform as an open-source project on GitHub under Apache 2.0 license, with the aim that it will facilitate and streamline more large-scale ML applications and research projects.
The subgraph-centric programming model is a promising approach and has been applied in many state-of-the-art distributed graph computing frameworks. However, traditional graph partition algorithms have significant difficulties in processing large-scale power-law graphs. The major problem is the communication bottleneck found in many subgraph-centric frameworks. Detailed analysis indicates that the communication bottleneck is caused by the huge communication volume or the extreme message imbalance among partitioned subgraphs. The traditional partition algorithms do not consider both factors at the same time, especially on power-law graphs. In this paper, we propose a novel efficient and balanced vertex-cut graph partition algorithm (EBV) which grants appropriate weights to the overall communication cost and communication balance. We observe that the number of replicated vertices and the balance of edge and vertex assignment have a great influence on communication patterns of distributed subgraph-centric frameworks, which further affect the overall performance. Based on this insight, We design an evaluation function that quantifies the proportion of replicated vertices and the balance of edges and vertices assignments as important parameters. Besides, we sort the order of edge processing by the sum of end-vertices degrees from small to large. Experiments show that EBV reduces replication factor and communication by at least 21.8% and 23.7% respectively than other self-based partition algorithms. When deployed in the subgraph-centric framework, it reduces the running time on power-law graphs by an average of 16.8% compared with the state-of-the-art partition algorithm. Our results indicate that EBV has a great potential in improving the performance of subgraph-centric frameworks for the parallel large-scale power-law graph processing.
Log in to be able to interact and post comments
comments
Fetching comments
Sign in to be able to follow your search criteria
