Participants of the Berlin Summit on Earth Virtualization Engines (EVEs)...
Multi-tenancy is essential for unleashing SmartNIC's potential in
datace...
Graph Neural Networks (GNNs) are a powerful tool for handling structured...
We introduce Graph of Thoughts (GoT): a framework that advances promptin...
Deep learning algorithms are increasingly employed at the edge. However,...
Execution graphs of parallel loop programs exhibit a nested, repeating
s...
Quantum computers offer a new paradigm of computing with the potential t...
The current hardware landscape and application scale is driving performa...
Vector search has emerged as the foundation for large-scale information
...
Recent advances in large language model (LLM) pretraining have led to
hi...
Dataflow devices represent an avenue towards saving the control and data...
With the rise of specialized hardware and new programming languages, cod...
Graph databases (GDBs) are crucial in academic and industry applications...
Serverless functions provide elastic scaling and a fine-grained billing
...
Sparse linear algebra is crucial in many application domains, but challe...
Data transfers are essential in today's computing systems as latency and...
Gradient preconditioning is a key technique to integrate the second-orde...
As deep learning models grow, sparsity is becoming an increasingly criti...
Performance optimization is an increasingly challenging but often repeti...
In this paper, we present PolarStar, a novel family of diameter-3 networ...
Recent advances in deep learning base on growing model sizes and the
nec...
In this humorous and thought provoking article, we discuss certain myths...
As the accuracy of machine learning models increases at a fast rate, so ...
Developers of networked systems often work with low-level RDMA libraries...
Pipeline parallelism enables efficient training of Large Language Models...
2.5D integration is an important technique to tackle the growing cost of...
Chips with hundreds to thousands of cores require scalable networks-on-c...
Many data have an underlying dependence on spatial location; it may be
w...
While quantum computers promise to solve some scientifically and commerc...
Generative Pre-trained Transformer (GPT) models set themselves apart thr...
Cloud computing represents an appealing opportunity for cost-effective
d...
Weather and climate simulations produce petabytes of high-resolution dat...
Graph databases (GDBs) enable processing and analysis of unstructured,
c...
The multi-pumping resource sharing technique can overcome the limitation...
The exponentially growing model size drives the continued success of dee...
With the application of machine learning to security-critical and sensit...
Numerous microarchitectural optimizations unlocked tremendous processing...
Important graph mining problems such as Clustering are computationally
d...
In this paper we present PolarFly, a diameter-2 network topology based o...
Post-processing ensemble prediction systems can improve weather forecast...
Optimizing application performance in today's hardware architecture land...
High-performance clusters and datacenters pose increasingly demanding
re...
Multilinear algebra kernel performance on modern massively-parallel syst...
Graph neural networks (GNNs) are among the most powerful tools in deep
l...
We present a new parallel model of computation suitable for spatial
arch...
Earth system models are developed with a tight coupling to target hardwa...
Numerical codes that require arbitrary precision floating point (APFP)
n...
FaaS (Function-as-a-Service) brought a fundamental shift into cloud
comp...
Triangle count and local clustering coefficient are two core metrics for...
This paper presents a security analysis of the InfiniBand architecture, ...