Relative efficiency of XLA for element-wise operations (hollow = vector operations; filled = matrix operations; circles = f64; triangles = f32; black = PC CPU; red = PC GPU; blue = HPC CPU; magenta = HPC GPU).

MASSIVELY PARALLEL LATENT SEMANTIC ANALYSES USING A GRAPHICS PROCESSING UNIT

Cavanagh, J.
Cui, S.

January 2009

Latent Semantic Analysis (LSA) aims to reduce the dimensions of large term-document datasets using S...

Investigation of the performance of LU decomposition method using CUDA

Ozcan, Caner
Sen, Baha

December 2012

AbstractIn recent years, parallel processing has been widely used in the computer industry. Software...

Accelerating BLAS and LAPACK via efficient floating point architecture design

Merchant, Farhad
Chattopadhyay, Anupam
Raha, Soumyendu
Nandy, S. K.
Narayan, Ranjani

January 2017

Basic Linear Algebra Subprograms (BLAS) and Linear Algebra Package (LAPACK) form basic building bloc...

Relative efficiency of XLA for numerical models (hollow = f64; filled = f32; circles = HEAT1D; triangles = HEAT2D; squares = NS2D; black = PC CPU; red = PC GPU; blue = HPC CPU; magenta = HPC GPU).

Xuzhen He (14676114)

February 2023

Relative efficiency of XLA for numerical models (hollow = f64; filled = f32; circles = HEAT1D; trian...

Model for the effective bandwidth of element-wise operations (lines = model prediction by Eq 11; symbols = measured maximal effective bandwidth; filled symbols = matrix operations; hollow symbols = vector operations; circles = CPU; triangles = GPU; black = medium f64; red = medium f32; blue = large f64; magenta = large f32).

Xuzhen He (14676114)

February 2023

Model for the effective bandwidth of element-wise operations (lines = model prediction by Eq 11; sym...

Optimal implementation of vector operations on the GPU platform (single precision; solid black line = benchmarked bandwidth; lines + symbols = effective bandwidth; black = COPY1D; red = XPXPY20_1D; filled circles = CUDA f32, hollow triangles = CUDA float4; filled squares = XLA).

Xuzhen He (14676114)

February 2023

Optimal implementation of vector operations on the GPU platform (single precision; solid black line ...

The examined operations.

Xuzhen He (14676114)

February 2023

The recent dramatic progress in machine learning is partially attributed to the availability of high...

The GPU computing efficiency of the three different thread arrangements in comparison with the original MATLAB implementation.

Wei-Jen Wang (427148)
I-Fan Hsieh (427149)
Chun-Chuan Chen (427150)

June 2013

The GPU computing efficiency of the three different thread arrangements in comparison with the or...

Optimal implementation of matrix operations on the CPU platform (double precision; solid black line = benchmarked bandwidth; lines + symbols = effective bandwidth; black = COPY2D; red = XPXPY20_2D; filled circles = OpenMP parallel for with cached access; hollow triangles = OpenMP parallel for with uncached access; filled diamonds = optimal implementation for corresponding vector operations; filled squares = XLA).

Xuzhen He (14676114)

February 2023

Optimal implementation of matrix operations on the CPU platform (double precision; solid black line ...

Information of the tested GPU platforms.

Xuzhen He (14676114)

February 2023

The recent dramatic progress in machine learning is partially attributed to the availability of high...

Hardware parameters of optimal implementations.

Xuzhen He (14676114)

February 2023

The recent dramatic progress in machine learning is partially attributed to the availability of high...

Optimal implementation of vector operations on the CPU platform (double precision; solid black line = benchmarked bandwidth; lines + symbols = effective bandwidth; black = COPY1D; red = XPXPY20_1D; filled circles = OpenMP parallel for with the maximal no. of threads, hollow circles = OpenMP parallel for with one thread; hollow triangles = single-thread SIMD; filled squares = XLA).

Xuzhen He (14676114)

February 2023

Optimal implementation of vector operations on the CPU platform (double precision; solid black line ...

Information of the tested CPU platforms.

Xuzhen He (14676114)

February 2023

The recent dramatic progress in machine learning is partially attributed to the availability of high...

Pseudocodes for the operations.

Xuzhen He (14676114)

February 2023

The recent dramatic progress in machine learning is partially attributed to the availability of high...

S1 Data -

Xuzhen He (14676114)

February 2023

The recent dramatic progress in machine learning is partially attributed to the availability of high...

MASSIVELY PARALLEL LATENT SEMANTIC ANALYSES USING A GRAPHICS PROCESSING UNIT

Cavanagh, J.
Cui, S.

January 2009

Latent Semantic Analysis (LSA) aims to reduce the dimensions of large term-document datasets using S...

Investigation of the performance of LU decomposition method using CUDA

Ozcan, Caner
Sen, Baha

December 2012

AbstractIn recent years, parallel processing has been widely used in the computer industry. Software...

Accelerating BLAS and LAPACK via efficient floating point architecture design

Merchant, Farhad
Chattopadhyay, Anupam
Raha, Soumyendu
Nandy, S. K.
Narayan, Ranjani

January 2017

Basic Linear Algebra Subprograms (BLAS) and Linear Algebra Package (LAPACK) form basic building bloc...

Relative efficiency of XLA for numerical models (hollow = f64; filled = f32; circles = HEAT1D; triangles = HEAT2D; squares = NS2D; black = PC CPU; red = PC GPU; blue = HPC CPU; magenta = HPC GPU).

Xuzhen He (14676114)

February 2023

Relative efficiency of XLA for numerical models (hollow = f64; filled = f32; circles = HEAT1D; trian...

Model for the effective bandwidth of element-wise operations (lines = model prediction by Eq 11; symbols = measured maximal effective bandwidth; filled symbols = matrix operations; hollow symbols = vector operations; circles = CPU; triangles = GPU; black = medium f64; red = medium f32; blue = large f64; magenta = large f32).

Xuzhen He (14676114)

February 2023

Model for the effective bandwidth of element-wise operations (lines = model prediction by Eq 11; sym...

Optimal implementation of vector operations on the GPU platform (single precision; solid black line = benchmarked bandwidth; lines + symbols = effective bandwidth; black = COPY1D; red = XPXPY20_1D; filled circles = CUDA f32, hollow triangles = CUDA float4; filled squares = XLA).

Xuzhen He (14676114)

February 2023

Optimal implementation of vector operations on the GPU platform (single precision; solid black line ...

The examined operations.

Xuzhen He (14676114)

February 2023

The recent dramatic progress in machine learning is partially attributed to the availability of high...

The GPU computing efficiency of the three different thread arrangements in comparison with the original MATLAB implementation.

Wei-Jen Wang (427148)
I-Fan Hsieh (427149)
Chun-Chuan Chen (427150)

June 2013

The GPU computing efficiency of the three different thread arrangements in comparison with the or...

Optimal implementation of matrix operations on the CPU platform (double precision; solid black line = benchmarked bandwidth; lines + symbols = effective bandwidth; black = COPY2D; red = XPXPY20_2D; filled circles = OpenMP parallel for with cached access; hollow triangles = OpenMP parallel for with uncached access; filled diamonds = optimal implementation for corresponding vector operations; filled squares = XLA).

Xuzhen He (14676114)

February 2023

Optimal implementation of matrix operations on the CPU platform (double precision; solid black line ...

Information of the tested GPU platforms.

Xuzhen He (14676114)

February 2023

The recent dramatic progress in machine learning is partially attributed to the availability of high...

Hardware parameters of optimal implementations.

Xuzhen He (14676114)

February 2023

The recent dramatic progress in machine learning is partially attributed to the availability of high...

Optimal implementation of vector operations on the CPU platform (double precision; solid black line = benchmarked bandwidth; lines + symbols = effective bandwidth; black = COPY1D; red = XPXPY20_1D; filled circles = OpenMP parallel for with the maximal no. of threads, hollow circles = OpenMP parallel for with one thread; hollow triangles = single-thread SIMD; filled squares = XLA).

Xuzhen He (14676114)

February 2023

Optimal implementation of vector operations on the CPU platform (double precision; solid black line ...

Information of the tested CPU platforms.

Xuzhen He (14676114)

February 2023

The recent dramatic progress in machine learning is partially attributed to the availability of high...

Pseudocodes for the operations.

Xuzhen He (14676114)

February 2023

The recent dramatic progress in machine learning is partially attributed to the availability of high...

S1 Data -

Xuzhen He (14676114)

February 2023

The recent dramatic progress in machine learning is partially attributed to the availability of high...

MASSIVELY PARALLEL LATENT SEMANTIC ANALYSES USING A GRAPHICS PROCESSING UNIT

Cavanagh, J.
Cui, S.

January 2009

Latent Semantic Analysis (LSA) aims to reduce the dimensions of large term-document datasets using S...

Investigation of the performance of LU decomposition method using CUDA

Ozcan, Caner
Sen, Baha

December 2012

AbstractIn recent years, parallel processing has been widely used in the computer industry. Software...

Accelerating BLAS and LAPACK via efficient floating point architecture design

Merchant, Farhad
Chattopadhyay, Anupam
Raha, Soumyendu
Nandy, S. K.
Narayan, Ranjani

January 2017

Basic Linear Algebra Subprograms (BLAS) and Linear Algebra Package (LAPACK) form basic building bloc...

Relative efficiency of XLA for element-wise operations (hollow = vector operations; filled = matrix operations; circles = f64; triangles = f32; black = PC CPU; red = PC GPU; blue = HPC CPU; magenta = HPC GPU).

Abstract

Extracted data

Relative efficiency of XLA for element-wise operations (hollow = vector operations; filled = matrix operations; circles = f64; triangles = f32; black = PC CPU; red = PC GPU; blue = HPC CPU; magenta = HPC GPU).

Abstract

Extracted data

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