About
NVIDIA Parabricks provides high-performance GPU-based software solutions for the analysis…
Experience
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
Education
-
-
-
Advisor: Prof. Ali Afzali-Kusha
Thesis title: Dynamic power management and voltage/frequency scaling in nano-scale devices -
-
Publications
-
APOGEE: Adaptive Prefetching On GPUs for Energy Efficiency
PACT 2013
Modern graphics processing units (GPUs) combine large amounts of parallel hardware with fast context switching among thousands of active threads to achieve high performance. However, such designs do not translate well to mobile environments where power constraints often limit the amount of hardware. In this work, we investigate the use of prefetching as a means to increase the energy efficiency of GPUs. Classically, CPU prefetching results in higher performance but worse energy efficiency due…
Modern graphics processing units (GPUs) combine large amounts of parallel hardware with fast context switching among thousands of active threads to achieve high performance. However, such designs do not translate well to mobile environments where power constraints often limit the amount of hardware. In this work, we investigate the use of prefetching as a means to increase the energy efficiency of GPUs. Classically, CPU prefetching results in higher performance but worse energy efficiency due to unnecessary data being brought on chip. Our approach, called APOGEE, uses an adaptive mechanism to dynamically detect and adapt to the memory access patterns found in both graphics and scientific applications that are run on modern GPUs to achieve prefetching efficiencies of over 90%. Rather than examining threads in isolation, APOGEE uses
adjacent threads to more efficiently identify address patterns and dynamically adapt the timeliness of prefetching. The net effect of APOGEE is that fewer thread contexts are necessary to hide memory latency and thus sustain performance. This reduction in thread contexts and related hardware translates to simplification of hardware and leads to a reduction in power. For Graphics and GPGPU applications, APOGEE enables an 8X reduction in multithreading
hardware, while providing a performance benefit of 19%. This translates to a 52% increase in performance per watt over systems with high multi-threading and 33% over existing
GPU prefetching techniques.Other authors -
Courses
-
Advanced Compilers
583
-
Introduction to Artificial Intelligence
492
-
Linear Programing
510
-
Microarchitecture
573
-
Parallel Computer Architecture
570
Languages
-
Persian
Native or bilingual proficiency
-
French
Elementary proficiency
-
English
Full professional proficiency
Other similar profiles
Explore collaborative articles
We’re unlocking community knowledge in a new way. Experts add insights directly into each article, started with the help of AI.
Explore MoreOthers named Mehrzad Samadi
-
Mehrzad Samadi
Computational designer | Researcher
-
Mehrzad Samadi
Student at University of Tehran - Kish International campus
2 others named Mehrzad Samadi are on LinkedIn
See others named Mehrzad Samadi