Related papers: ScaleSimulator: A Fast and Cycle-Accurate Parallel…
Deterministic execution offers many benefits for debugging, fault tolerance, and security. Running parallel programs deterministically is usually difficult and costly, however - especially if we desire system-enforced determinism, ensuring…
The Simplex tableau has been broadly used and investigated in the industry and academia. With the advent of the big data era, ever larger problems are posed to be solved in ever larger machines whose architecture type did not exist in the…
Large-scale distributed computing infrastructures such as the Worldwide LHC Computing Grid (WLCG) require comprehensive simulation tools for evaluating performance, testing new algorithms, and optimizing resource allocation strategies.…
Skyline computation is an essential database operation that has many applications in multi-criteria decision making scenarios such as recommender systems. Existing algorithms have focused on checking point domination, which lack efficiency…
This paper presents a dynamic, adaptive, and scalable framework for simulating task scheduling on the edge of the Internet of Things called "SchEdge". This simulator is designed to be highly configurable to reflect the detailed…
In this paper, we describe the algorithms we implemented in FDPS to make efficient use of accelerator hardware such as GPGPUs. We have developed FDPS to make it possible for many researchers to develop their own high-performance parallel…
Many computer systems for calculating the proper organization of memory are among the most critical issues. Using a tier cache memory (along with branching prediction) is an effective means of increasing modern multi-core processors'…
We propose a parallel algorithm for local, on the fly, model checking of a fragment of CTL that is well-suited for modern, multi-core architectures. This model-checking algorithm takes bene t from a parallel state space construction…
Deep learning (DL) models are piquing high interest and scaling at an unprecedented rate. To this end, a handful of tiled accelerators have been proposed to support such large-scale training tasks. However, these accelerators often…
Parallel multigrid is widely used as preconditioners in solving large-scale sparse linear systems. However, the current multigrid library still needs more satisfactory performance for structured grid problems regarding speed and…
The increasing demands for computing performance have been a reality regardless of the requirements for smaller and more energy efficient devices. Throughout the years, the strategy adopted by industry was to increase the robustness of a…
To train modern large DNN models, pipeline parallelism has recently emerged, which distributes the model across GPUs and enables different devices to process different microbatches in pipeline. Earlier pipeline designs allow multiple…
Our computers today, from sophisticated servers to small smartphones, operate based on the same computing model, which requires running a sequence of discrete instructions, specified as an algorithm. This sequential computing paradigm has…
ARCHYTAS aims to design and evaluate non-conventional hardware accelerators, in particular, optoelectronic, volatile and non-volatile processing-in-memory, and neuromorphic, to tackle the power, efficiency, and scalability bottlenecks of AI…
As modern analogue/mixed-signal design increasingly relies on optimization-in-the-loop flows, such as AI and LLM-based sizing agents that repeatedly invoke SPICE-efficient, accurate high-performance simulators have become an indispensable…
Many organisations have a large network of connected computers, which at times may be idle. These could be used to run larger data processing problems were it not for the difficulty of organising and managing the deployment of such…
We study how parallelism can speed up quantum simulation. A parallel quantum algorithm is proposed for simulating the dynamics of a large class of Hamiltonians with good sparse structures, called uniform-structured Hamiltonians, including…
High-performance computing systems are more and more often based on accelerators. Computing applications targeting those systems often follow a host-driven approach in which hosts offload almost all compute-intensive sections of the code…
High-performance computing (HPC) applications are increasingly executed in heterogeneous environments, introducing new challenges for programming and software portability. SYCL has emerged as a leading model designed to simplify…
A parallel computer system is a collection of processing elements that communicate and cooperate to solve large computational problems efficiently. To achieve this, at first the large computational problem is partitioned into several tasks…