arXiv subjects

Mohamed Assem Ibrahim

Publications and source records attributed to Mohamed Assem Ibrahim.

6 recordsLinked to original sources

arXiv2026

DMA-Latte: Expanding the Reach of DMA Offloads to Latency-bound ML Communication

Offloading communication to existing direct memory access (DMA) engines, available on most state-of-the-art commercial GPUs, has emerged as a practical and low-cost solution to efficiently overlap computation and communication in machine learning (ML). However, the reach of DMA offloads has so far been limited to bandwidth-bound scenarios only (10s of MB to GB transfer sizes). In this work, we break this barrier and extend DMA communication offloads to latency-bound regions (KB to low MB). Specifically, we leverage hitherto untapped features available in the state-of-the-art AMD Instinct$^{\mathrm{TM}}$ GPUs that render DMA communication offloads competitive even in latency-bound regions. We demonstrate the efficacy of these features both at the operator level (ML communication collectives such as all-gather and all-to-all), and at the end-to-end workload level (LLM inference). At the operator level, our optimizations provide up to 4.5$\times$ speedups (3.2$\times$ geomean in the latency-bound region) over baseline DMA offload, narrowing the performance gap while delivering additional power savings (3-10%) for ML collectives compared to state-of-the-art GPU core-based communication library, RCCL. At the workload level, we demonstrate acceleration for LLM inference: up to 1.65$\times$ lower latency and up to 1.9$\times$ higher throughput over the state-of-the-art vLLM inference framework. We conclude with a discussion of AMD Instinct GPU runtime innovations that stand to expose these features.

cs.DC↗

arXiv2026

CompPow: A Case for Component-level GPU Power Management

The ever increasing demand for ML-driven intelligence in a wide spectrum of domains has led to ubiquity of GPUs. At the same time, GPUs are notorious for their power consumption needs and often dominate power allocation in a typical ML datacenter. While datacenter-level power optimizations which focus on collection of GPUs are promising, in this work, we take a different tack -- namely, we take a closer look at power consumption inside a GPU. Specifically, as modern GPUs are comprised of integrated components, we make a case for component-awareness, termed CompPow in this work, for improved power management in modern GPUs. We demonstrate for a variety of ML operations and execution patterns, CompPow has the potential to deliver higher energy efficiency (10%) and even improved performance (5%). We conclude with recommendations on how component-aware software-hardware co-design can extract additional energy efficiency from modern GPUs.

cs.AR↗

arXiv2025

FinGraV: Methodology for Fine-Grain GPU Power Visibility and Insights

Ubiquity of AI makes optimizing GPU power a priority as large GPU-based clusters are often employed to train and serve AI models. An important first step in optimizing GPU power consumption is high-fidelity and fine-grain power measurement of key AI computations on GPUs. To this end, we observe that as GPUs get more powerful, the resulting sub-millisecond to millisecond executions make fine-grain power analysis challenging. In this work, we first carefully identify the challenges in obtaining fine-grain GPU power profiles. To address these challenges, we devise FinGraV methodology where we employ execution time binning, careful CPU-GPU time synchronization, and power profile differentiation to collect fine-grain GPU power profiles across prominent AI computations and across a spectrum of scenarios. Using the said FinGraV power profiles, we provide both, guidance on accurate power measurement and, in-depth view of power consumption on state-of-the-art AMD Instinct MI300X. For the former, we highlight a methodology for power differentiation across executions. For the latter, we make several observations pertaining to GPU sub-component power consumption and GPU power proportionality across different scenarios. We believe that FinGraV unlocks both an accurate and a deeper view of power consumption of GPUs and opens up avenues for power optimization of these ubiquitous accelerators.

cs.AR↗

arXiv2024

Balanced Data Placement for GEMV Acceleration with Processing-In-Memory

With unprecedented demand for generative AI (GenAI) inference, acceleration of primitives that dominate GenAI such as general matrix-vector multiplication (GEMV) is receiving considerable attention. A challenge with GEMVs is the high memory bandwidth this primitive demands. Multiple memory vendors have proposed commercially viable processing-in-memory (PIM) prototypes that attain bandwidth boost over processor via augmenting memory banks with compute capabilities and broadcasting same command to all banks. While proposed PIM designs stand to accelerate GEMV, we observe in this work that a key impediment to truly harness PIM acceleration is deducing optimal data-placement to place the matrix in memory banks. To this end, we tease out several factors that impact data-placement and propose PIMnast methodology which, like a gymnast, balances these factors to identify data-placements that deliver GEMV acceleration. Across a spectrum of GenAI models, our proposed PIMnast methodology along with additional orchestration knobs we identify delivers up to 6.86$\times$ speedup for GEMVs (of the available 7$\times$ roofline speedup) leading to up to 5$\times$ speedup for per-token latencies.

cs.AR↗

arXiv2023

Just-in-time Quantization with Processing-In-Memory for Efficient ML Training

Data format innovations have been critical for machine learning (ML) scaling, which in turn fuels ground-breaking ML capabilities. However, even in the presence of low-precision formats, model weights are often stored in both high-precision and low-precision during training. Furthermore, with emerging directional data formats (e.g., MX9, MX6, etc.) multiple low-precision weight copies can be required. To lower memory capacity needs of weights, we explore just-in-time quantization (JIT-Q) where we only store high-precision weights in memory and generate low-precision weights only when needed. To perform JIT-Q efficiently, in this work, we evaluate emerging processing-in-memory (PIM) technology to execute quantization. With PIM, we can offload quantization to in-memory compute units enabling quantization to be performed without incurring costly data movement while allowing quantization to be concurrent with accelerator computation. Our proposed PIM-offloaded quantization keeps up with GPU compute and delivers considerable capacity savings (up to 24\%) at marginal throughput loss (up to 2.4\%). Said memory capacity savings can unlock several benefits such as fitting larger model in the same system, reducing model parallelism requirement, and improving overall ML training efficiency.

cs.AR↗

arXiv2023

Collaborative Acceleration for FFT on Commercial Processing-In-Memory Architectures

This paper evaluates the efficacy of recent commercial processing-in-memory (PIM) solutions to accelerate fast Fourier transform (FFT), an important primitive across several domains. Specifically, we observe that efficient implementations of FFT on modern GPUs are memory bandwidth bound. As such, the memory bandwidth boost availed by commercial PIM solutions makes a case for PIM to accelerate FFT. To this end, we first deduce a mapping of FFT computation to a strawman PIM architecture representative of recent commercial designs. We observe that even with careful data mapping, PIM is not effective in accelerating FFT. To address this, we make a case for collaborative acceleration of FFT with PIM and GPU. Further, we propose software and hardware innovations which lower PIM operations necessary for a given FFT. Overall, our optimized PIM FFT mapping, termed Pimacolaba, delivers performance and data movement savings of up to 1.38$\times$ and 2.76$\times$, respectively, over a range of FFT sizes.

cs.AR↗