2013

Derin Öğrenme Algoritmalarının GPU ve CPU Donanım Mimarileri Üzerinde Uygulanması ve Performans Analizi: Deneysel Araştırma

Günümüzde hızla gelişen teknolojiyle verilerin çeşitliliği ve boyutu artmaktadır. Bu artış bilgisayar mimarisinde farklı tasarımları ortaya çıkarmıştır. CPU ve GPU mimarileri üzerlerinde bulunan çekirdek sayıları uygulama anında sonuca ulaşmada çözümler sağlayabilmektedir. Yazılım geliştirmesi yapılırken işlem performansı ve güç tüketimine dikkat edilmelidir. CPU’lar GPU’lardan daha uzun işlem süresi ile uygulamaları yürütmektedir. Bu süre performans sırasında harcanan gücü doğru orantılı etkilemektedir. GPU’lar derin öğrenme algoritmalarında CPU’lardan daha hızlı ve başarılı sonuçlar vermektedir. Öğrenme aşamasındaki en önemli kriter olan veri setinin büyüklüğü ve çeşitliliği öğrenme başarısını aynı oranda artırmaktadır. Bu çalışmada farklı mimariye sahip işlemciler üzerinde veri seti büyüklüğü ve işlem süresi kriterleri göz önünde bulundurularak uygulamalar yapılmıştır. Yapılan uygulamalarda GPU mimarilerinde harcanan güç seviyesi ölçülmüştür. Farklı büyüklüğe sahip 3 veri seti üzerinde CNN, RNN ve LSTM derin öğrenme algoritmaları uygulanmıştır. 6 farklı deney yapılarak performans ve enerji tüketimi konularında tespitler ve performans karşılaştırılması yapılmıştır. Çalışma neticesinde elde edilen sonuçlar ile algoritmalar üzerinde çalışmalar yapılırken süre ve enerji kriterleri baz alınmıştır. Bulgular derin öğrenme algoritmalarının yüksek doğrulukta GPU sistemlerinde tahmin edilmesinde yardımcı bir araç olarak kullanılabileceği yönündedir. Araştırmanın sonuçları CPU ve GPU sistemleri ile enerji ve süre açısından önemli bilgiler içermesinin yanı sıra, gelecekte farklı sektörlerde uygulanması açısından değer taşımaktadır.

Anahtar Kelimeler:

Derin Öğrenme, CPU, GPU, Bilgisayar Mimarisi, Performans Analizi

Application of Deep Learning Algorithms to GPU and CPU Hardware Architectures and Performance Analysis: Experimental Reseach

Nowadays, the variety and volume of data is increasing with the rapidly developing technology. This increase has led to different designs in computer architecture. The number of cores on CPU and GPU architectures can provide solutions to achieve the result at the time of application. When developing software, attention should be paid to processing performance and power consumption. CPUs run applications with a longer processing time than GPUs. This time directly affects power consumption during performance. GPUs produce faster and more successful results than CPUs for Deep Learning algorithms. The size and diversity of the dataset, which is the most important criterion in the learning phase, increases the learning success to the same extent. In this study, applications were performed on processors with different architectures considering the criteria of dataset size and processing time. In the applications, the power consumption of GPU architectures is measured. CNN, RNN and LSTM deep learning algorithms are applied to 3 different sized datasets. 6 different experiments are performed and determinations and performance comparisons are made on the performance and power consumption. Time and energy criteria were used in processing the algorithms with the results of the study. The results show that Deep Learning algorithms can be used as a tool for predicting GPU systems with high accuracy. The results of the study not only contain important information related to CPU and GPU systems, energy, and time, but also are valuable for future applications in various fields.

Keywords:

Deep Learning, CPU, GPU, Computer Architecture, Performance Analysis,

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