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- Harmonia: Balancing Compute and Memory Power in High-Performance GPUs
Using these sensitivity predictors, we propose a two-level coordinated power management scheme, Harmonia, which coordinates the hardware power states of the GPU and the memory system
- Harmonia: Balancing compute and memory power in high-performance GPUs - IEEE Xplore
In this paper, we address the problem of efficiently managing the relative power demands of a high-performance GPU and its memory subsystem We develop a manage
- Characterizing Power and Performance Of GPU Memory Access
In this work we investigate the power and performance characteristics of various GPU memory accesses We take an empirical approach and experimentally examine and evaluate how GPU power and performance vary with data access patterns and software parameters including GPU thread block size
- Harmonia: balancing compute and memory power in high-performance GPUs: ACM SIGARCH Computer Architecture News: Vol 43, No 3S
In this paper, we address the problem of efficiently managing the relative power demands of a high-performance GPU and its memory subsystem We develop a management approach that dynamically tunes the hardware operating configurations to maintain balance between the power dissipated in compute versus memory access across GPGPU application phases
- CUDA英伟达GPU 计算能力表（Compute Capability） - 知乎
计算能力（Compute Capability）并不是指gpu的计算性能。 nvidia发明计算能力这个概念是为了标识设备的核心架构、gpu硬件支持的功能和指令，因此计算能力也被称为“SM version"。
- GPU MODE Lecture 4: Compute and Memory Basics
Lecture #4 provides an overview of CUDA programming fundamentals, focusing on compute architecture, memory management, and optimization strategies like kernel fusion and tiling to achieve high-performance GPU computation
- Balancing Compute and Memory Power in High-Performance Gpus
Re relative power consumption of GPU cores and the memory sults show that Harmonia improves measured energy-delay system, with the relative compute and memory demands of squared (ED2) by up to 36% (12% on average) with negli the applications
- Characterizing power and performance of GPU memory access
In this work we investigate the power and performance characteristics of various GPU memory accesses We take an empirical approach and experimentally examine and evaluate how GPU power and performance vary with data access patterns and software parameters including GPU thread block size
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