92 citations · 237 across the 29 of their papers we have counts for
31 papers
MLCommons Jailbreak Benchmark v1.0
Carsten Maple, Cagatay Yucel, Isaac Holeman +33
Modern AI systems are designed to refuse hazardous requests. A jailbreak is a prompt crafted to bypass those safeguards and elicit outputs that the system would normally refuse to…
Pluralis v0.1: Towards a Multicultural, Multimodal, Multilingual Benchmark for AI Risk and Reliability
Alicia Parrish, Rajat Shinde, Sanket Badhe +57
Current AI safety evaluation and benchmarking frameworks predominantly rely on Western-centric culture-agnostic defaults that mask critical regional laws, socio-linguistic nuances,…
Swimba: Switch Mamba Model Scales State Space Models
Zhixu Du, Krishna Teja Chitty-Venkata, Murali Emani +3
Mixture-of-experts (MoE) is a common approach for increasing parameter capacity, but applying MoE to state space model (SSM) token mixers can multiply the cost of the recurrent sta…
AI Benchmark Democratization and Carpentry
Gregor von Laszewski, Wesley Brewer, Jeyan Thiyagalingam +28
Benchmarks are a cornerstone of modern machine learning, enabling reproducibility, comparison, and scientific progress. However, AI benchmarks are increasingly complex, requiring d…
ImageNet-Think-250K: A Large-Scale Synthetic Dataset for Multimodal Reasoning for Vision Language Models
Krishna Teja Chitty-Venkata, Murali Emani
We develop ImageNet-Think, a multimodal reasoning dataset designed to aid the development of Vision Language Models (VLMs) with explicit reasoning capabilities. Our dataset is buil…
Foundation Models for Discovery and Exploration in Chemical Space
Alexius Wadell, Anoushka Bhutani, Victor Azumah +26
Accurate prediction of atomistic, thermodynamic, and kinetic properties from molecular structures underpins materials innovation. Existing computational and experimental approaches…
AERIS: Argonne Earth Systems Model for Reliable and Skillful Predictions
Väinö Hatanpää, Eugene Ku, Jason Stock +12
Generative machine learning offers new opportunities to better understand complex Earth system dynamics. Recent diffusion-based methods address spectral biases and improve ensemble…
PagedEviction: Structured Block-wise KV Cache Pruning for Efficient Large Language Model Inference
Krishna Teja Chitty-Venkata, Jie Ye, Xian-He Sun +4
KV caching significantly improves the efficiency of Large Language Model (LLM) inference by storing attention states from previously processed tokens, enabling faster generation of…
LExI: Layer-Adaptive Active Experts for Efficient MoE Model Inference
Krishna Teja Chitty-Venkata, Sandeep Madireddy, Murali Emani +1
Mixture-of-Experts (MoE) models scale efficiently by activating only a subset of experts per token, offering a computationally sparse alternative to dense architectures. While prio…
MoPEQ: Mixture of Mixed Precision Quantized Experts
Krishna Teja Chitty-Venkata, Jie Ye, Murali Emani
Large Language and Vision Models using a Mixture-of-Experts (MoE) architecture pose significant challenges for deployment due to their computational and memory demands. Mixed Preci…
PreLoRA: Hybrid Pre-training of Vision Transformers with Full Training and Low-Rank Adapters
Krishu K Thapa, Reet Barik, Krishna Teja Chitty-Venkata +2
Training large models ranging from millions to billions of parameters is highly resource-intensive, requiring significant time, compute, and memory. It is observed that most of the…
Aurora: Architecting Argonne's First Exascale Supercomputer for Accelerated Scientific Discovery
William E. Allcock, Benjamin S. Allen, James Anchell +106
Aurora is Argonne National Laboratory's pioneering Exascale supercomputer, designed to accelerate scientific discovery with cutting-edge architectural innovations. Key new technolo…
MoE-Inference-Bench: Performance Evaluation of Mixture of Expert Large Language and Vision Models
Krishna Teja Chitty-Venkata, Sylvia Howland, Golara Azar +5
Mixture of Experts (MoE) models have enabled the scaling of Large Language Models (LLMs) and Vision Language Models (VLMs) by achieving massive parameter counts while maintaining c…
LangVision-LoRA-NAS: Neural Architecture Search for Variable LoRA Rank in Vision Language Models
Krishna Teja Chitty-Venkata, Murali Emani, Venkatram Vishwanath
Vision Language Models (VLMs) integrate visual and text modalities to enable multimodal understanding and generation. These models typically combine a Vision Transformer (ViT) as a…
AILuminate: Introducing v1.0 of the AI Risk and Reliability Benchmark from MLCommons
Shaona Ghosh, Heather Frase, Adina Williams +99
The rapid advancement and deployment of AI systems have created an urgent need for standard safety-evaluation frameworks. This paper introduces AILuminate v1.0, the first comprehen…
BaKlaVa -- Budgeted Allocation of KV cache for Long-context Inference
Ahmed Burak Gulhan, Krishna Teja Chitty-Venkata, Murali Emani +2
In Large Language Model (LLM) inference, Key-Value (KV) caches (KV-caches) are essential for reducing time complexity. However, they result in a linear increase in GPU memory as th…
LLM-Inference-Bench: Inference Benchmarking of Large Language Models on AI Accelerators
Krishna Teja Chitty-Venkata, Siddhisanket Raskar, Bharat Kale +6
Large Language Models (LLMs) have propelled groundbreaking advancements across several domains and are commonly used for text generation applications. However, the computational de…
AI-coupled HPC Workflow Applications, Middleware and Performance
Wes Brewer, Ana Gainaru, Frédéric Suter +3
AI integration is revolutionizing the landscape of HPC simulations, enhancing the importance, use, and performance of AI-driven HPC workflows. This paper surveys the diverse and ra…
DeepSpeed4Science Initiative: Enabling Large-Scale Scientific Discovery through Sophisticated AI System Technologies
Shuaiwen Leon Song, Bonnie Kruft, Minjia Zhang +89
In the upcoming decade, deep learning may revolutionize the natural sciences, enhancing our capacity to model and predict natural occurrences. This could herald a new era of scient…
A Comprehensive Performance Study of Large Language Models on Novel AI Accelerators
Murali Emani, Sam Foreman, Varuni Sastry +6
Artificial intelligence (AI) methods have become critical in scientific applications to help accelerate scientific discovery. Large language models (LLMs) are being considered as a…
HPC-GPT: Integrating Large Language Model for High-Performance Computing
Xianzhong Ding, Le Chen, Murali Emani +6
Large Language Models (LLMs), including the LLaMA model, have exhibited their efficacy across various general-domain natural language processing (NLP) tasks. However, their perform…
Data Race Detection Using Large Language Models
Le Chen, Xianzhong Ding, Murali Emani +3
Large language models (LLMs) are demonstrating significant promise as an alternate strategy to facilitate analyses and optimizations of high-performance computing programs, circumv…
A Survey of Techniques for Optimizing Transformer Inference
Krishna Teja Chitty-Venkata, Sparsh Mittal, Murali Emani +2
Recent years have seen a phenomenal rise in performance and applications of transformer neural networks. The family of transformer networks, including Bidirectional Encoder Represe…
LM4HPC: Towards Effective Language Model Application in High-Performance Computing
Le Chen, Pei-Hung Lin, Tristan Vanderbruggen +3
In recent years, language models (LMs), such as GPT-4, have been widely used in multiple domains, including natural language processing, visualization, and so on. However, applying…