1,184 papers · page 6 of 60
Parallel multigrid methods are widely used as preconditioned for solving large sparse linear systems. Most multigrids rely on general sparse matrix formats, which prevent them from achieving optimal performance. There is an emerging trend towards semi-structured multigrids that b…
We study the binomial, trinomial, and Black-Scholes-Merton models of option pricing. We present fast parallel discrete-time finite-difference algorithms for American call option pricing under the binomial and trinomial models and American put option pricing under the Black-Schole…
Concurrent data structures often require additional memory for handling synchronization issues in addition to memory for storing elements. Depending on the amount of this additional memory, implementations can be more or less memory-friendly. A memory-optimal implementation enjoy…
Introducing correct and optimal OpenMP parallelization directives in applications is a challenge. To parallelize a loop in an input application code automatically, parallelizing compilers need to disprove dependences with respect to variables across iterations of the loop. Perfor…
Recent work has shown how to augment any CAS-based concurrent data structure to support taking a snapshot of the current memory state. Taking the snapshot, as well as loads and CAS (Compare and Swap) operations, take constant time. Importantly, such snapshotting can be used to ea…
Tensor Core Unit (TCU) is increasingly integrated into modern high-performance processors to enhance matrix multiplication performance. However, constrained to its over-specification, its potential for improving other critical scientific operations like stencil computations remai…
Protein structure prediction helps to understand gene translation and protein function, which is of growing interest and importance in structural biology. The AlphaFold model, which used transformer architecture to achieve atomic-level accuracy in protein structure prediction, wa…
Two distinguishing features of state-of-the-art mobile and autonomous systems are: 1) There are often multiple workloads, mainly deep neural network (DNN) inference, running concurrently and continuously. 2) They operate on shared memory System-on-Chips (SoC) that embed heterogen…
Integer sorting is a fundamental problem in computer science. This paper studies parallel integer sort both in theory and in practice. In theory, we show tighter bounds for a class of existing practical integer sort algorithms, which provides a solid theoretical foundation for th…
Distributed large model inference is still in a dilemma where balancing cost and effect. The online scenarios demand intraoperator parallelism to achieve low latency and intensive communications makes it costly. Conversely, the inter-operator parallelism can achieve high throughp…
Sparse tensor contraction (SpTC) serves as an essential operation in high-performance applications. The high dimensionality of sparse tensors makes SpTC fundamentally challenging in aspects such as costly multidimensional index search, extensive intermediate output data, and indi…
In this paper, we present an integrated framework composed of a highly efficient phase field simulator and an in-situ feature extraction library. This novel framework enables us to conduct extreme-scale micro-structure evolution simulations while the characteristic features of ea…
Processing large-scale graphs with billions to trillions of edges requires efficiently utilizing parallel systems. However, current graph processing engines do not scale well beyond a few tens of computing nodes because they are oblivious to the communication cost variations acro…
We propose a novel approach to iterated sparse matrix dense matrix multiplication, a fundamental computational kernel in scientific computing and graph neural network training. In cases where matrix sizes exceed the memory of a single compute node, data transfer becomes a bottlen…
Recommendation models are an important category of deep learning models whose size is growing enormous. They consist of a sparse part with TBs of memory footprint and a dense part that demands PFLOPs of computing capability to train. Unfortunately, the high sparse communication c…
Neural Network Molecular Dynamics (NNMD) has become a major approach in material simulations, which can speedup the molecular dynamics (MD) simulation for thousands of times, while maintaining ab initio accuracy, thus has a potential to fundamentally change the paradigm of materi…
GPU-aware collective communication has become a major bottleneck for modern computing platforms as GPU computing power rapidly rises. To address this issue, traditional approaches integrate lossy compression directly into GPU-aware collectives, which still suffer from serious iss…
We present a verification-based Byzantine Fault Tolerant processing system, called OsirisBFT, for distributed task-parallel applications. OsirisBFT treats computation tasks differently from state update tasks, allowing the application to scale independently from number of expecte…
Kronecker Matrix-Matrix Multiplication (Kron-Matmul) is the multiplication of a matrix with the Kronecker Product of several smaller matrices. Kron-Matmul is a core operation for many scientific and machine learning computations. State-of-the-art Kron-Matmul implementations utili…
High-precision integer multiplication is crucial in privacy-preserving computational techniques but poses acceleration challenges on GPUs due to its complexity and the diverse bit lengths in cryptosystems. This paper introduces GIM, an efficient high-precision integer multiplicat…