26,098 papers · page 388 of 1,305
This paper is the first to specify blockchains as a composition of abstract data types all together with a hierarchy of consistency criteria that formally characterizes the histories admissible for distributed programs that use them. The paper presents as well some results on imp…
We engineer a GPU implementation of a B-Tree that supports concurrent queries (point, range, and successor) and updates (insertions and deletions). Our B-tree outperforms the state of the art, a GPU log-structured merge tree (LSM) and a GPU sorted array. In particular, point and …
Open nesting allows replacing the automatic detection of conflicting memory accesses used in transactional memory (TM) with programmer-specified higher-level conflict detection. Higher-level conflict detection allows removing conflicts of commuting operations where the automatic …
With the rapid change of computing architectures, and variety of programming models; the ability to develop performance portable applications has become of great importance. This is particularly true in large production codes where developing and maintaining hardware specific ver…
Non-volatile memory (NVM) promises persistent main memory that remains correct despite loss of power. Since caches are expected to remain volatile, concurrent algorithms must be redesigned to ensure a consistent state after a system crash, and to continue the execution upon recov…
In this tutorial participants learn how to build their own parallel programming language features by developing them as language extensions in the ableC [4] extensible C compiler framework. By implementing new parallel programming abstractions as language extensions one can build…
Asynchronous task-based programming models are gaining popularity to address programmability and performance challenges in high performance computing. One of the main attractions of these models and runtimes is their potential to automatically expose and exploit overlap of comput…
Scale-out has become the standard answer to data analysis, machine learning and many other fields. Contrary to common belief, scale-up machines can outperform scale-out clusters for a considerable portion of tasks. However, those scale-up machines are not economical and may not b…
This paper develops an operational semantics for a release-acquire fragment of the C11 memory model with relaxed accesses. We show that the semantics is both sound and complete with respect to the axiomatic model of Batty et al. The semantics relies on a per-thread notion of obse…
Efficient inference of deep learning models are challenging and of great value in both academic and industrial community. In this paper, we focus on exploiting the sparsity in input data to improve the performance of deep learning models. We propose an end-to-end optimization pip…
We define local transactional race freedom (LTRF), which provides a programmer model for software transactional memory. LTRF programs satisfy the SC-LTRF property, thus allowing the programmer to focus on sequential executions in which transactions execute atomically. Unlike prev…
In this work, we present a bottleneck-aware sparse matrix-vector multiplication auto-tuner (BASMAT) for general purpose graphics processing units (GPGPUs) that targets both fast execution and low preprocessing overheads.
Concurrent data structures are widely used in modern multicore architectures, providing atomicity (linearizability) for each concurrent operation. However, it is often desirable to execute several operations on multiple data structures atomically. We present a design of a transac…
The hardware transactional memory (HTM) implementations in commercially available processors are significantly hindered by their tight capacity constraints. In practice, this renders current HTMs unsuitable to many real-world workloads of in-memory databases.
Throughput-oriented architectures, such as GPUs, can sustain three orders of magnitude more concurrent threads than multicore architectures. This level of concurrency pushes typical synchronization primitives (e.g., mutexes) over their scalability limits, creating significant per…
Profiling feedback is an important technique used by developers for performance debugging, where it is usually used to pinpoint performance bottlenecks and also to find optimization opportunities. Assessing the validity and potential benefit of a program transformation requires a…
Ultra-deep neural network(UDNN) tends to yield higher-quality model but its training process is often difficult to handle. Scarce GPU DRAM capacity is the primary bottleneck that limits the depth of neural network and the range of trainable minibatch size. In this paper, we prese…
Compilation techniques for nested-parallel applications that can adapt to hardware and dataset characteristics are vital for unlocking the power of modern hardware. This paper proposes such a technique, which builds on flattening and is applied in the context of a functional data…
Deep learning (DL) research yields accuracy and product improvements from both model architecture changes and scale: larger data sets and models, and more computation. For hardware design, it is difficult to predict DL model changes. However, recent prior work shows that as datas…
We present Min-Rounds BC (MRBC), a distributed-memory algorithm in the CONGEST model that computes the betweenness centrality (BC) of every vertex in a directed unweighted n-node graph in O(n) rounds. Min-Rounds BC also computes all-pairs-shortest-paths (APSP) in such graphs. It …