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Durable techniques coupled with transactional semantics provide to application developers the guarantee that data is saved consistently in persistent memory (PM), even in the event of a non-corrupting failure. Persistence fences and flush instructions ...

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 ...

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 ...

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 ...

Collective communication algorithms are an important component of distributed computation. Indeed, in the case of deep-learning, collective communication is the Amdahl's bottleneck of data-parallel training.

This paper introduces SCCL (for Synthesized ...

Binary code analysis is widely used to help assess a program's correctness, performance, and provenance. Binary analysis applications often construct control flow graphs, analyze data flow, and use debugging information to understand how machine code ...

Recent works from both hardware and software domains offer various optimizations that try to take advantage of near data computing (NDC) opportunities. While the results from these works indicate performance improvements of various magnitudes, the ...

We present dynamic control replication, a run-time program analysis that enables scalable execution of implicitly parallel programs on large machines through a distributed and efficient dynamic dependence analysis. Dynamic control replication ...

Graph Neural Network (GNN) has recently drawn a rapid increase of interest in many domains for its effectiveness in learning over graphs. Maximizing its performance is essential for many tasks, but remains preliminarily understood. In this work, we ...

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 ...

With the development of the big data ecosystem, large-scale data analytics has become more prevalent in the past few years. Apache Spark, etc., provide a flexible approach for scalable processing upon massive data. However, they are not designed for ...

Hypergraph partitioning is used in many problem domains including VLSI design, linear algebra, Boolean satisfiability, and data mining. Most versions of this problem are NP-complete or NP-hard, so practical hypergraph partitioners generate approximate ...

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. ...

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 ...

Dynamic lock-free data structures require a memory reclamation scheme with a similar progress. Until today, lock-free schemes are applied to data structures on a case-by-case basis, often with algorithm modifications to the data structure.

In this paper ...

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 ...

Multitask learning has proven to be useful in the field of machine learning when additional knowledge is available to help a prediction task. We adapt this paradigm to develop autotuning frameworks, where the objective is to find the optimal performance ...

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 ...

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 ...

Nvidia Tensor Cores achieve high performance with half-precision matrix inputs tailored towards deep learning workloads. However, this limits the application of Tensor Cores especially in the area of scientific computing with high precision ...

Sparse Matrix-Vector multiplication (SpMV) is an essential kernel for parallel numerical applications. SpMV displays sparse and irregular data accesses, which complicate its vectorization. Such difficulties make SpMV to frequently experiment non-optimal ...

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 ...

Sparse tensor contractions appear commonly in many applications. Efficiently computing a two sparse tensor product is challenging: It not only inherits the challenges from common sparse matrix-matrix multiplication (SpGEMM), i.e., indirect memory access ...

Task-based programming models like OmpSs-2 and OpenMP provide a flexible data-flow execution model to exploit dynamic, irregular and nested parallelism. Providing an efficient implementation that scales well with small granularity tasks remains a ...

Task-parallel programs often enjoy deadlock freedom under certain restrictions, such as the use of structured join operations, as in Cilk and X10, or the use of asynchronous task futures together with deadlock-avoiding policies such as Known Joins or ...

Aggressive technology scaling trends have worsened the transient fault problem in high-performance computing (HPC) systems. Some faults are benign, but others can lead to silent data corruption (SDC), which represents a serious problem; a fault ...

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 ...

The transformer is the most critical algorithm innovation of the Nature Language Processing (NLP) field in recent years. Unlike the Recurrent Neural Network (RNN) models, transformers are able to process on dimensions of sequence lengths in parallel, ...

Performance models are well-known instruments to understand the scaling behavior of parallel applications. They express how performance changes as key execution parameters, such as the number of processes or the size of the input problem, vary. Besides ...

Fifty years of parallel programming has generated a substantial legacy parallel codebase, creating a new portability challenge: re-parallelizing already parallel code. Our solution exploits inherently portable parallel patterns, and addresses the ...