Multiple - Block Ahead

نویسندگان

  • Pascal Sainrat
  • Pierre Michaud
چکیده

A basic rule in computer architecture is that a processor cannot execute an application faster than it fetches its instructions. This paper presents a novel cost-eeective mechanism called the two-block ahead branch predictor. Information from the current instruction block is not used for predicting the address of the next instruction block, but rather for predicting the block following the next instruction block. This approach overcomes the instruction fetch bottleneck exhibited by wide-dispatch \brainiac" processors by enabling them to eeciently predict addresses of two instruction blocks in a single cycle. Furthermore, pipelin-ing the branch prediction process can also be done by means of our predictor for \speed demon" processors to achieve higher clock rate or to improve the prediction accuracy by means of bigger prediction structures. Moreover, and unlike the previously-proposed multiple predictor schemes, multiple-block ahead branch pre-dictors can use any of the branch prediction schemes to perform the very accurate predictions required to achieve high-performance on superscalar processors.

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

Presenting a model for Multiple-step-ahead-Forecasting of volatility and Conditional Value at Risk in fossil energy markets

Fossil energy markets have always been known as strategic and important markets. They have a significant impact on the macro economy and financial markets of the world. The nature of these markets are accompanied by sudden shocks and volatility in the prices. Therefore, they must be controlled and forecasted by using appropriate tools. This paper adopts the Generalized Auto Regressive Condition...

متن کامل

A Fast Jump Ahead Algorithm for Linear Recurrences in a Polynomial Space

Linear recurring sequences with very large periods are widely used as the basic building block of pseudorandom number generators. In many simulation applications, multiple streams of random numbers are needed, and these multiple streams are normally provided by jumping ahead in the sequence to obtain starting points that are far apart. For maximal-period generators having a large state space, t...

متن کامل

Unfolded Deep Recurrent Convolutional Neural Network with Jump Ahead Connections for Acoustic Modeling

Recurrent neural networks (RNNs) with jump ahead connections have been used in the computer vision tasks. Still, they have not been investigated well for automatic speech recognition (ASR) tasks. In other words, unfolded RNN has been shown to be an effective model for acoustic modeling tasks. This paper investigates how to elaborate a sophisticated unfolded deep RNN architecture in which recurr...

متن کامل

A Parallel GNFS Algorithm Based on a Reliable Look-Ahead Block Lanczos Method for Integer Factorization

The Rivest-Shamir-Adleman (RSA) algorithm is a very popular and secure public key cryptosystem, but its security relies on the difficulty of factoring large integers. The General Number Field Sieve (GNFS) algorithm is currently the best known method for factoring large integers over 110 digits. Our previous work on the parallel GNFS algorithm, which integrated the Montgomery’s block Lanczos met...

متن کامل

A Block Toeplitz Look - Ahead Schur

This paper gives a look-ahead Schur algorithm for nding the symmetric factorization of a Hermitian block Toeplitz matrix. The method is based on matrix operations and does not require any relations with orthogonal polynomials. The simplicity of the matrix based approach ought to shed new light on other issues such as parallelism and numerical stability.

متن کامل

ذخیره در منابع من


  با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

عنوان ژورنال:

دوره   شماره 

صفحات  -

تاریخ انتشار 1996