1,184 papers · page 11 of 60
Recent desktop and mobile processors often integrate CPU and GPU onto the same die. The limited memory bandwidth of these integrated architectures can negatively affect the performance of data-parallel workloads when all computational resources are active. The combination of acti…
Rust is a modern systems language focused on performance and reliability. Complementing Rust's promise to provide "fearless concurrency", developers frequently exploit asynchronous message passing. Unfortunately, sending and receiving messages in an arbitrary order to maximise co…
Triangle Counting (TC) is a basic graph mining problem with numerous applications. However, the large size of real-world graphs has a severe effect on TC performance.
Concurrent program verification is challenging due to a large number of thread interferences. A popular approach is to encode concurrent programs as SMT formulas and then rely on off-the-shelf SMT solvers to accomplish the verification. In most existing works, an SMT solver is si…
The availability of Non-Volatile Main Memory (known as NVMM) enables the design of recoverable concurrent algorithms. We study the power of software combining in achieving recoverable synchronization and designing persistent data structures. Software combining is a general synchr…
In this paper, we describe a parallel Branch-and-Bound (B&B) algorithm with a history-based domination technique, and we apply it to the Sequential Ordering Problem (SOP). To the best of our knowledge, the proposed algorithm is the first parallel B&B algorithm that includes a his…
High-performance computing, together with a neural network model trained from data generated with first-principles methods, has greatly boosted applications of ab initio molecular dynamics in terms of spatial and temporal scales on modern supercomputers. Previous state-of-the-art…
The current trend in deep learning is to scale models to extremely large sizes with the objective of increasing their accuracy. Mixture-of-Expert (MoE) is the most popular pre-trained model that makes feasible the training of models with parameters beyond trillion-scale. Thanks t…
With the ever-shrinking size of transistors and increasing scale of applications, silent data corruptions (SDCs) have become a common yet serious issue in HPC applications. Selective instruction duplication (SID) is a popular fault-tolerance technique that can obtain a high SDC c…
The accompanying poster to this short paper presents a combination of reverse mode AD and formal methods to enable efficient differentiation of (or backpropagation through) shared-memory parallel code. Compared to the state of the art, our approach can more often avoid the need f…
Performance analysis is widely used to identify performance issues of parallel applications. However, complex communications and data dependence, as well as the interactions between different kinds of performance issues make high-efficiency performance analysis even harder. Altho…
Real-time data processing applications with low latency requirements have led to the increasing popularity of stream processing systems. While such systems offer convenient APIs that can be used to achieve data parallelism automatically, they offer limited support for computation…
In this paper we introduce Jiffy, the first lock-free, linearizable, ordered key-value index that offers both (1) batch updates, i.e., put and remove operations that are executed atomically, and (2) consistent snapshots used by, e.g., range scan operations. Jiffy is built as a mu…
The increasing demand in HPC to utilize accelerators has motivated the development of pragma-based directives to target these devices. OmpSs-2 and OpenACC are both directive-based solutions that allow application programmers to utilize accelerators. The two leverage distinct type…
We present a framework that allows programmers to specify replicated data stores through application logic, data replication scheme, and high-level invariants that needs to be satisfied. From such specifications all the needed consistency requirements can be inferred from traces …
The pursuit of power-efficiency is popularizing asymmetric multicore processors (AMP) such as ARM big.LITTLE, Apple M1 and recent Intel Alder Lake with big and little cores. However, we find that existing scalable locks fail to scale on AMP and cause collapses in either throughpu…
Large-scale pretrained AI models have shown state-of-the-art accuracy in a series of important applications. As the size of pretrained AI models grows dramatically each year in an effort to achieve higher accuracy, training such models requires massive computing and memory capabi…
Computing the product of two sparse matrices (SpGEMM) is a fundamental operation in various combinatorial and graph algorithms as well as various bioinformatics and data analytics applications for computing inner-product similarities. For an important class of algorithms, only a …
We present bundled references, a new building block to provide linearizable range query operations for highly concurrent lock-based linked data structures. Bundled references allow range queries to traverse a path through the data structure that is consistent with the target atom…
Sparse general matrix-matrix multiplication (SpGEMM) is one of the most fundamental building blocks in sparse linear solvers, graph processing frameworks and machine learning applications. The existing parallel approaches for shared memory SpGEMM mostly use the row-row style with…