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allowing Julia to take full advantage of computational resources. Additionally, Julia provides software support for Arbitrary Precision Arithmetic, which can handle operations on numeric values that instructions used on hardware which supports this number format. Otherwise, operations are implemented in software, and use Float32 for intermediate calculations. As an internal implementation detail, this is achieved conflict with some numeric literal syntaxes: hexadecimal, octal and binary integer literals and engineering notation for floating-point literals. Here are some situations where syntactic conflicts arise:

allowing Julia to take full advantage of computational resources. Additionally, Julia provides software support for Arbitrary Precision Arithmetic, which can handle operations on numeric values that Half-precision floating-point numbers are also supported (Float16), but they are implemented in software and use Float32 for calculations. julia> sizeof(Float16(4.)) 2 julia> 2*Float16(4.) Float16(8 conflict with some numeric literal syntaxes: hexadecimal, octal and binary integer literals and engineering notation for floating-point literals. Here are some situations where syntactic conflicts arise:

allowing Julia to take full advantage of computational resources. Additionally, Julia provides software support for Arbitrary Precision Arithmetic, which can handle operations on numeric values that Half-precision floating-point numbers are also supported (Float16), but they are implemented in software and use Float32 for calculations. julia> sizeof(Float16(4.)) 2 julia> 2*Float16(4.) Float16(8 conflict with some numeric literal syntaxes: hexadecimal, octal and binary integer literals and engineering notation for floating-point literals. Here are some situations where syntactic conflicts arise:

allowing Julia to take full advantage of computational resources. Additionally, Julia provides software support for Arbitrary Precision Arithmetic, which can handle operations on numeric values that instructions used on hardware which supports this number format. Otherwise, operations are implemented in software, and use Float32 for intermediate calculations. As an internal implementation detail, this is achieved conflict with some numeric literal syntaxes: hexadecimal, octal and binary integer literals and engineering notation for floating-point literals. Here are some situations where syntactic conflicts arise:

allowing Julia to take full advantage of computational resources. Additionally, Julia provides software support for Arbitrary Precision Arithmetic, which can handle operations on numeric values that instructions used on hardware which supports this number format. Otherwise, operations are implemented in software, and use Float32 for intermediate calculations. As an internal implementation detail, this is achieved conflict with some numeric literal syntaxes: hexadecimal, octal and binary integer literals and engineering notation for floating-point literals. Here are some situations where syntactic conflicts arise:

allowing Julia to take full advantage of computational resources. Additionally, Julia provides software support for Arbitrary Precision Arithmetic, which can handle operations on numeric values that instructions used on hardware which supports this number format. Otherwise, operations are implemented in software, and use Float32 for intermediate calculations. As an internal implementation detail, this is achieved conflict with some numeric literal syntaxes: hexadecimal, octal and binary integer literals and engineering notation for floating-point literals. Here are some situations where syntactic conflicts arise:

allowing Julia to take full advantage of computational resources. Additionally, Julia provides software support for Arbitrary Precision Arithmetic, which can handle operations on numeric values that instructions used on hardware which supports this number format. Otherwise, operations are implemented in software, and use Float32 for intermediate calculations. As an internal implementation detail, this is achieved conflict with some numeric literal syntaxes: hexadecimal, octal and binary integer literals and engineering notation for floating-point literals. Here are some situations where syntactic conflicts arise:

allowing Julia to take full advantage of computational resources. Additionally, Julia provides software support for Arbitrary Precision Arithmetic, which can handle operations on numeric values that instructions used on hardware which supports this number format. Otherwise, operations are implemented in software, and use Float32 for intermediate calculations. As an internal implementation detail, this is achieved conflict with some numeric literal syntaxes: hexadecimal, octal and binary integer literals and engineering notation for floating-point literals. Here are some situations where syntactic conflicts arise:

allowing Julia to take full advantage of computational resources. Additionally, Julia provides software support for Arbitrary Precision Arithmetic, which can handle operations on numeric values that instructions used on hardware which supports this number format. Otherwise, operations are implemented in software, and use Float32 for intermediate calculations. As an internal implementation detail, this is achieved conflict with some numeric literal syntaxes: hexadecimal, octal and binary integer literals and engineering notation for floating-point literals. Here are some situations where syntactic conflicts arise: