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Posts

Feb, 24

Seamless acceleration of Fortran intrinsics via AMD AI engines

A major challenge that the HPC community faces is how to continue delivering the performance demanded by scientific programmers, whilst meeting an increased emphasis on sustainable operations. Specialised architectures, such as FPGAs and AMD’s AI Engines (AIEs), have been demonstrated to provide significant energy efficiency advantages, however a major challenge is that to most effectively […]
Feb, 24

Forecasting time series with constraints

Time series forecasting presents unique challenges that limit the effectiveness of traditional machine learning algorithms. To address these limitations, various approaches have incorporated linear constraints into learning algorithms, such as generalized additive models and hierarchical forecasting. In this paper, we propose a unified framework for integrating and combining linear constraints in time series forecasting. Within […]
Feb, 24

Evaluating the Performance of the DeepSeek Model in Confidential Computing Environment

The increasing adoption of Large Language Models (LLMs) in cloud environments raises critical security concerns, particularly regarding model confidentiality and data privacy. Confidential computing, enabled by Trusted Execution Environments (TEEs), offers a promising solution to mitigate these risks. However, existing TEE implementations, primarily CPU-based, struggle to efficiently support the resource-intensive nature of LLM inference and […]
Feb, 24

The AI CUDA Engineer: Agentic CUDA Kernel Discovery, Optimization and Composition

Recent advances in Large Language Models have driven large-scale deployment, resulting in ever-growing inference time and energy demand. While manual optimization of low-level code implementations is feasible, it is an arduous task that requires deep expertise to balance the complex interplay of algorithmic, software, and hardware bottlenecks. This report presents the first comprehensive agentic framework […]
Feb, 16

cuSZp2: A GPU Lossy Compressor with Extreme Throughput and Optimized Compression Ratio

Existing GPU lossy compressors suffer from expensive data movement overheads, inefficient memory access patterns, and high synchronization latency, resulting in limited throughput. This work proposes CUSZP2, a generic single-kernel error-bounded lossy compressor purely on GPUs designed for applications that require high speed, such as large-scale GPU simulation and large language model training. In particular, CUSZP2 […]
Feb, 16

Leveraging LLVM OpenMP GPU Offload Optimizations for Kokkos Applications

OpenMP provides a cross-vendor API for GPU offload that can serve as an implementation layer under performance portability frameworks like the Kokkos C++ library. However, recent work identified some impediments to performance with this approach arising from limitations in the API or in the available implementations. Advanced programming concepts such as hierarchical parallelism and use […]
Feb, 16

InfiniteHiP: Extending Language Model Context Up to 3 Million Tokens on a Single GPU

In modern large language models (LLMs), handling very long context lengths presents significant challenges as it causes slower inference speeds and increased memory costs. Additionally, most existing pre-trained LLMs fail to generalize beyond their original training sequence lengths. To enable efficient and practical long-context utilization, we introduce InfiniteHiP, a novel, and practical LLM inference framework […]
Feb, 16

Teaching An Old Dog New Tricks: Porting Legacy Code to Heterogeneous Compute Architectures With Automated Code Translation

Legacy codes are in ubiquitous use in scientific simulations; they are well-tested and there is significant time investment in their use. However, one challenge is the adoption of new, sometimes incompatible computing paradigms, such as GPU hardware. In this paper, we explore using automated code translation to enable execution of legacy multigrid solver code on […]
Feb, 16

Vortex: Overcoming Memory Capacity Limitations in GPU-Accelerated Large-Scale Data Analytics

Despite the high computational throughput of GPUs, limited memory capacity and bandwidth-limited CPU-GPU communication via PCIe links remain significant bottlenecks for accelerating large-scale data analytics workloads. This paper introduces Vortex, a GPU-accelerated framework designed for data analytics workloads that exceed GPU memory capacity. A key aspect of our framework is an optimized IO primitive that […]
Feb, 10

Optimizing the optimizer increasing performance efficiency of modern compilers

A long-standing goal, which is increasingly important in the post-Moore era, is to augment system performance by building more intelligent compilers. One of our motivating hypotheses is that much of the capability needed to advance compiler optimization is already present: state-of-the-art compilers not only provide a large set of code transformations, but also (by-and-large) correctly […]
Feb, 10

Demystifying Cost-Efficiency in LLM Serving over Heterogeneous GPUs

Recent advancements in Large Language Models (LLMs) have led to increasingly diverse requests, accompanied with varying resource (compute and memory) demands to serve them. However, this in turn degrades the cost-efficiency of LLM serving as common practices primarily rely on homogeneous GPU resources. In response to this problem, this work conducts a thorough study about […]
Feb, 10

Compiler Support for Speculation in Decoupled Access/Execute Architectures

Irregular codes are bottlenecked by memory and communication latency. Decoupled access/execute (DAE) is a common technique to tackle this problem. It relies on the compiler to separate memory address generation from the rest of the program, however, such a separation is not always possible due to control and data dependencies between the access and execute […]

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