26,098 papers · page 295 of 1,305
Manually optimizing the tradeoffs between accuracy, performance and energy for resource-intensive applications with flexible accuracy or precision requirements is extremely difficult. We present ApproxTuner, an automatic framework for accuracy-aware optimization of tensor-based a…
Many-to-many mapping models for user- to kernel-level threads (or "M:N threads") have been extensively studied for decades as a lightweight substitute for current Pthreads implementations that provide a simple one-to-one mapping ("1:1 threads"). M:N threads derive performance fro…
In-situ parallel workflows couple multiple component applications via streaming data transfer to avoid data exchange via shared file systems. Such workflows are challenging to configure for optimal performance due to the huge space of possible configurations. Here, we propose an …
Safe memory reclamation (SMR) algorithms suffer from a trade-off between bounding unreclaimed memory and the speed of reclamation. Hazard pointer (HP) based algorithms bound unreclaimed memory at all times, but tend to be slower than other approaches. Epoch based reclamation (EBR…
Nonblocking data structures face a safe memory reclamation (SMR) problem. In these algorithms, a node removed from the data structure cannot be reclaimed (freed) immediately, as other threads may be about to access it. The goal of an SMR scheme is to minimize the number of remove…
The lock-free, ordered, singly linked list as proposed in [5, 8] is a textbook example of a concurrent data structure [6, 10]. The data structure supports lock-free insertion and deletion, and wait-free contains operations on items identified by a unique key. The lock-free implem…
This paper presents a new Single Source Shortest Path (SSSP) algorithm for GPUs. Our key advancement is an improved work scheduler, which is central to the performance of SSSP algorithms. Previous GPU solutions for SSSP use simple work schedulers that can be implemented efficient…
Traversals are commonly seen in tree data structures, and performance-enhancing transformations between tree traversals are critical for many applications. Existing approaches to reasoning about tree traversals and their transformations are ad hoc, with various limitations on the…
Given a concurrent data structure, we present an approach for efficiently taking snapshots of its constituent CAS objects. More specifically, we support a constant-time operation that returns a snapshot handle. This snapshot handle can later be used to read the value of any base …
Dynamic memory management on GPUs is generally understood to be a challenging topic. On current GPUs, hundreds of thousands of threads might concurrently allocate new memory or free previously allocated memory. This leads to problems with thread contention, synchronization overhe…
Convolutional neural networks (CNNs), as representatives of deep learning, are one of the most commonly used neural networks in applications such as graphic image analysis. However, CNN has heavy computation patterns; network training processes could take several hours even with …
With the magnitude of graph-structured data continually increasing, graph processing systems that can scale-out and scale-up are needed to handle extreme-scale datasets. While existing distributed out-of-core solutions have made it possible, they suffer from limited performance d…
This paper investigates the problem of integrating two powerful abstractions for concurrent programming, namely futures and transactional memory. Our focus is on specifying the semantics of execution of "transactional futures", i.e., futures that execute as atomic transactions an…
Uncertain or probabilistic graphs have been ubiquitously used in many emerging applications. Previously CPU based techniques were proposed to use sampling but suffer from (1) low computation efficiency and large memory overhead, (2) low degree of parallelism, and (3) nonexistent …
Convolution is the most time-consuming part in the computation of convolutional neural networks (CNNs), which have achieved great successes in numerous practical applications. Due to the complex data dependency and the increase in the amount of model samples, the convolution suff…
We present optimizations to improve communication performance by reducing on-node data movement for a class of distributed memory applications. The primary concept is to eliminate the data movement associated with packing and unpacking subsets of the data during communication. Wi…
In this article, we introduce a new technique for precision tuning. This problem consists of finding the least data types for numerical values such that the result of the computation satisfies some accuracy requirement. State of the art techniques for precision tuning use a try a…
Abstract elided by the publisher.
Abstract elided by the publisher.
Abstract elided by the publisher.