Abstract:
A scalar processor that includes a plurality of scalar arithmetic logic units and a special function unit. Each scalar unit performs, in a different time interval, the same operation on a different data item, where each different time interval is one of a plurality of successive, adjacent time intervals. Each unit provides an output data item in the time interval in which the unit performs the operation and provides a processed data item in the last of the successive, adjacent time intervals. The special function unit provides a special function computation for the output data item of a selected one of the scalar units, in the time interval in which the selected scalar unit performs the operation, so as to avoid a conflict in use among the scalar units. A vector processing unit includes an input data buffer, the scalar processor, and an output orthogonal converter.

Description:
BACKGROUND OF THE INVENTION 
     1. Field of the Invention 
     The present invention generally relates to graphics vector processors and more particularly to a graphics processor with scalar arithmetic logic units (ALUs) capable of processing graphics vector data. 
     2. Description of the Prior Art 
     Graphics data can be represented in a vector format with components of geometry information (XYZW) or pixel value information (RGBA). Typically, the geometry engines used with these vectors process all of the components at once leading to complicated internal architecture and relatively high latency between data input and data output. The typical geometry engine is an important front-end part of any modern graphics accelerator. The speed of the geometry data processing affects the entire efficiency of the architecture of the graphics accelerator. 
     Recent graphics API developments require the support of particular instruction sets and define the hardware capabilities to process geometry and pixel value vectors. Because of these high performance requirements, current graphic engines are implemented as a unit that process all vector components in parallel with complicated input data and internal data crossbars. Furthermore, in order to meet these performance requirements, the graphics engines use multiple vector units in SIMD (Single Instruction, Multiple Data) or MIMD (Multiple Instruction, Multiple Data) architecture with additional hardware and time overhead. This leads to VLIW (Very Large Instruction Word) architecture with complex control and synchronization units supporting multithreaded execution of programs. 
     Referring to  FIG. 1 , a data flow  10  for a prior art vector processing unit is shown. A graphics vector  12  having components Xi, Yi, Zi, and Wi is inputted into a buffer memory  14 . Each graphics vector  12  is read sequentially from the buffer memory  14  into a vector ALU  16 . The single vector ALU  16  operates on each component of the vector  12  at the same time in parallel. The vector ALU  16  includes a special function unit  18  for performing special operations. The internal structure of the ALU  16  is large and complicated in order to perform operations on all four components (i.e., Xi, Yi, Zi, and Wi) of the vector  12 . Furthermore, the internal protocols and communication of the ALU  16  are complicated due to the parallel nature of the operations being performed. A final output vector  20  having components Xout, Yout, Zout, and Wout is generated by the vector ALU  16 . The architecture of the prior art vector processing unit can be considered parallel (full vector or horizontal) vector component flow because the components of each vector  12  are processed concurrently. 
     Referring to  FIG. 2 , a datapath representation for processing one set of data with the prior art vector processing unit is shown. In the example shown in  FIG. 2 , the function is: 
     
       
         
               
             
               
               
               
             
           
               
                   
               
             
             
               
                 vector Normalized_Difference (vector V1, vector V2) 
               
             
          
           
               
                 V1 −&gt; r0.xyzw 
                 V2 −&gt; r1.xyzw 
                 (xyzw - components of graphics data) 
               
               
                   
               
             
          
         
       
     
     The corresponding instructions for this function are: 
     
       
         
               
               
             
           
               
                   
               
             
             
               
                 SUB r2, r0, r1 
                 //subtraction of all components 
               
               
                 DP3 r3.x, r2, r2 
                 //dot product of 3 components (x, y, z) with result in 
               
               
                   
                 x-component 
               
               
                 RSQ r3.x, r3.x 
                 //reciprocal square root of result in x-component 
               
               
                 MUL r2, r2, r3.x 
                 //scaling all components with RSQ result 
               
               
                   
               
             
          
         
       
     
     Referring to  FIG. 2 , the first instruction cycle ( 1 ) performs the subtraction between r 0  and r 1  and generates output vector r 2  for each of the components x,y,z, and w. Next, in the second instruction cycle ( 2 ), the dot product is performed on r 2  itself with the result only in the x component such that r 3 . x  is generated. The reciprocal square root of r 3 . x  is operated upon in the third instruction cycle ( 3 ). As seen in  FIG. 2 , during the third instruction cycle ( 3 ), only the x component is being operated upon. Next, in the fourth instruction cycle ( 4 ), the r 2  components are scaled only by the x component (i.e., r 3 . x ) to generate the normalized vector difference r 2 . In order to process four sets of data, the process is repeated four times and would take a total of sixteen instruction cycles. 
     It can be seen that the prior art vector processing unit can be very complex due to the parallel processing of vector components. Accordingly, latency becomes an issue during the processing. Furthermore, the prior art vector processing unit needs a large instruction format with multiple bits to control the vector component routing and processing. Also, the prior art vector processing unit has a complex input data bus to support the required graphics API functionality. Also, data dependency detection by hardware or software is required when using the prior art vector processing unit. 
     The present invention addresses the deficiencies in the above-mentioned prior art vector processing units by providing a vector processing unit that uses scalar ALUs. Accordingly, the present invention provides a SIMD scalar processing unit which is less complex and smaller in size than the prior art units. Furthermore, the present invention provides a system whereby the instruction set is simpler than the prior art vector processing unit and latency is greatly reduced. 
     SUMMARY OF THE INVENTION 
     In accordance with the present invention there is provided a SIMD scalar processing unit for processing at least two vectors having multiple components. The SIMD scalar processing unit has an input data buffer for arranging the components of the vectors from a parallel vector component flow into a sequential vector component flow. The SIMD scalar processing unit further includes at least one scalar arithmetic logic unit operable to receive the components of the vectors from the input data buffer. The scalar arithmetic logic unit is operable to perform a function on each of the components of the vectors in order to determine a result. The SIMD scalar processing unit further includes an output converter operable to receive the results from the arithmetic logic unit. The output converter can rearrange the components back into a parallel vector component flow if it is required. 
     The SIMD scalar processing unit further includes a special function unit that is operable to communicate with the scalar arithmetic logic units. The special function unit can perform operations on the vector components that the scalar arithmetic logic units cannot. In this respect, the SIMD scalar processing unit will further include a multiplexer operative to receive vector components from each of the scalar arithmetic logic units and select a component for processing by the special function unit. 
     Typically, the vector will have i components and the scalar processing unit will have i scalar arithmetic logic units (SCU). Each of the scalar arithmetic logic units are subsequently (or serially) connected to one another such that an instruction sent to a first scalar arithmetic logic unit is delayed before being sent to a subsequent scalar arithmetic logic unit. Each of the scalar arithmetic logic units has at least one instruction delay register for delaying instructions to another arithmetic logic unit subsequently (or serially) connected thereto. Furthermore, address and control signals can be delayed to subsequent scalar arithmetic logic units. 
     The scalar arithmetic logic unit SCU further includes a datapath section for performing the operation on the component of the vector, and a control and address module for operating the datapath section. The scalar arithmetic logic unit SCU may further include at least one data delay register for delaying common data to another arithmetic logic unit subsequently (or serially) connected thereto. 
     In accordance with the present invention there is provided a method of processing at least two vectors having multiple components with a SIMD scalar processing unit. The method begins by arranging the components of the vectors from a parallel vector component flow into a sequential vector component flow with the input data buffer. Next, the operation is performed on a vector component with a respective one of the scalar arithmetic logic units in order to generate a result. Furthermore, the special function unit may perform an operation on the component. Finally, the components of the result are rearranged by the output converter into a parallel vector component flow. 
     In accordance with the present invention, there is provided a scalar arithmetic logic unit for a SIMD scalar processing unit which processes vector components. The scalar arithmetic logic unit can be subsequently (or serially) connected to another arithmetic logic unit of the scalar processing unit. The scalar arithmetic logic unit has a datapath section for performing operations on the vector components. Additionally, the scalar arithmetic logic unit has a delay register section for delaying the issuance of vector components to other arithmetic logic units subsequently (or serially) connected thereto. In accordance with the present invention, the delay register section of the scalar arithmetic logic unit may include a delay register for each vector component passing through the scalar arithmetic logic unit. The scalar arithmetic logic unit further includes an address and control module which is operative to control the datapath section. An address and control delay register of the scalar arithmetic logic unit can delay the timing of address and control signals to subsequent scalar arithmetic logic units connected thereto. Furthermore, the scalar arithmetic logic unit may have a common data delay register for delaying the timing of common data to the datapath section. 
     In accordance with the present invention, there is provided a SIMD processing unit for processing a vector having x, y, z, and w components. Each of the x, y, z, and w components has multiple values. The SIMD processing unit has an orthogonal access memory for arranging a parallel vector component flow of the multiple values for each component into a sequential vector component flow. The SIMD processing unit further includes a scalar processor in electrical communication with the orthogonal access memory. The scalar processor has a bank of scalar arithmetic logic units that are operable to perform an operation on each value of the component from the orthogonal access memory and generate a result. The scalar processor further includes a special function unit in electrical communication with the bank of scalar arithmetic logic units. The special function unit is operative to perform an operation on a result from one of the scalar arithmetic logic units and return the result to the same arithmetic logic unit. The SIMD processing unit further includes an output orthogonal converter in electrical communication with the scalar processor. The output orthogonal converter is operable to arrange the results from the scalar processor into a parallel vector component flow. 
     One embodiment of the present invention is a scalar processor that includes a plurality of scalar arithmetic logic units, a multiplexer, and a single special function unit. Each of the scalar units is operative to perform, in a different time interval, the same operation on a different data item, where each different time interval is one of a plurality of successive, adjacent time intervals, and where each unit provides an output data item in the time interval in which the unit performs said operation and each unit provides a processed data item in a last one of the successive, adjacent time intervals. The multiplexer is configured to provide the output data item from a selected one of the scalar units. The single special function unit is operable to provide a special function computation for the output data item of a selected one of the scalar units, in the time interval in which teh selected scalar unit performs the operation, so as to avoid a conflict in use among the scalar units. Each scalar unit has an address and control path for carrying address and control information that commands the operation, where the address and control path includes a delay element having a delay equal to the time interval, and where the address and control paths are connected in series such that the address and control information arrives at each unit in the time interval in which the scalar unit performs the operation. Each scalar unit has a data processing path and one or more delay paths, each of which includes a delay element having a delay equal to the time interval, connected in series with the data processing path such that each different data item arrives in the scalar unit in the interval in which the unit performs the operation and such that the processed data item from each unit is available in the last of the successive time intervals. 
     Another embodiment of the present invention is a scalar processor that includes a plurality of means for scalar processing, means for selecting one of the processing means to provide an output data item, and means for performing a special function computation for the output data item of the selected one of the scalar processing means. Each scalar processing means is operative to perform, in a different time interval, the same operation on a different data item, where each different time interval is one of a plurality of successive, adjacent time intervals. Each scalar processing means provides an output data item in the time interval in which the processing means performs the operation, and each scalar processing means provides a processed data item in a last one of the successive adjacent time intervals. The special function computation performing means performs a special function in the time interval in which the selected scalar processing means performs the operation so as to avoid a conflict in use among the plurality of processing means. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       These as well as other features of the present invention will become more apparent upon reference to the drawings wherein: 
         FIG. 1  is a data flow diagram for a prior art vector processing unit; 
         FIG. 2  is a datapath representation for processing one set of data with the prior art vector processing unit; 
         FIG. 3  illustrates a vector SIMD processing unit of the present invention; 
         FIG. 4  is a diagram showing the physical organization of the scalar processor of the present invention; 
         FIG. 5  is an instruction timing diagram for the vector SIMD processing unit of the present invention; 
         FIG. 6  is a circuit diagram of the internal structure of the scalar arithmetic and logic unit (SCU) shown in  FIG. 4 ; and 
         FIG. 7  is a datapath representation for processing one set of data with the vector processing unit of the present invention. 
     
    
    
     DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENT 
     Referring to the drawings wherein the showings are for purposes of illustrating a preferred embodiment of the present invention only, and not for purposes of limiting the same, FIG.  3  illustrates a SIMD vector processing unit  30  constructed in accordance with the present invention. A graphics vector  12  is inputted into an input data buffer  32  of the SIMD processing unit  30  in order to reorder the graphics vector  12  into the proper sequence. Specifically, the input data buffer  32  is a 4-bank orthogonal access memory which can output the components in a sequential (vertical) vector component flow. For instance, as seen in  FIG. 3 , the components of the vectors are rearranged such that the x components of each vector are outputted simultaneously. Therefore, X 1 , X 2 , X 3 , and X 4  are outputted simultaneously as the component vector  33 . Next, the Y components of each vector are outputted (i.e., Y 1 , Y 2 , Y 3 , and Y 4 ). This process continues for the Z and W components as well. In this regard, the vectors are converted from a parallel vector component flow to a sequential vector component flow by the input data buffer  32 . 
     The component vector  33  is inputted into a scalar processor  42  that has a bank of four scalar ALUs  34   a – 34   d  and a special function unit (SFU)  36 . Each of the ALUs  34   a – 34   d , as well as the SFU  36 , performs the desired operations on the vector components  33 . The processing of the components of the component vector  33  occurs in parallel by each of the scalar ALUs  34   a – 34   d , as will be further explained below. The scalar processor  42  generates a scalar output vector  40  that is fed into an output orthogonal converter  38 . The scalar output vector  40  must be rearranged in order to generate the output vector  20 . The converter  38  is a vertical register capable of processing all of the components of the vector  12  simultaneously. In this respect, the converter  38  will rearrange the vector components from the scalar processor  42  into the correct parallel vector component flow for the output vector  20 . The operation of the output orthogonal converter  38  is explained in greater detail in applicant&#39;s co-pending U.S. patent application “Synchronous Periodical Orthogonal Data Converter”, U.S. patent application Ser. No. 10/666,083, filed on Sep. 19, 2003 the contents of which are incorporated by reference herein. 
     Referring to  FIG. 4 , the physical organization of the scalar processor  42  is shown. The processor  42  has four identical scalar ALUs (SCU)  34   a – 34   d  and the special function unit  36 . Each of the scalar ALUs  34   a – 34   d  has four inputs I 0 –I 3  and four outputs O 0 –O 3 . Furthermore, each scalar ALU  34   a – 34   d  has a memory address input MA, a common data input C, a memory address output MO and a common data output CO. Additionally, each scalar ALU  34   a – 34   d  has a forward output FWD and a special function unit input SC. 
     The M bit individual components of each component vector  33  are inputted into a respective one of the inputs I 0 –I 3  of the scalar ALUs  34   a – 34   d . For example, if the component vector  33  contains the X components (i.e., X 1 , X 2 , X 3  and X 4 ), then the M bits of the first X component (i.e., X 1 ) are inputted into I 0  of the scalar ALU  34   a . Similarly, the M bits of the second X component X 2  are inputted into I 1  of the second scalar ALU  34   b , the M bits of the third X component X 3  are inputted into I 2  of the third scalar ALU  34   c , and the M bits of the fourth X component X 4  are inputted into I 3  of the fourth scalar ALU  34   d . The remaining inputs of each scalar ALU  34   a – 34   d  are connected to one of the outputs of that scalar ALU  34   a – 34   d . For example, for the first scalar ALU  34   a , output O 0  is connected to input I 3 , output O 2  is connected to input I 1 , and output O 3  is connected to input I 2 . The output O 1  is the final output and generates the first X component of the scalar ALU output vector  40 . It will be recognized that each of the other scalar ALUs  34   b – 34   d  have respective outputs connected to respective ones of the inputs according to  FIG. 4 . The manner of connection of the inputs I 0 –I 3  and O 0 –O 3  is individual for each scalar ALU  34   a – 34   d  such that it depends on the activity in each instruction cycle to the instruction diagram shown in  FIG. 5 . The scalar ALU  34   b  generates the second component of the scalar ALU output vector  40  at output O 2 , the third component of the scalar ALU output vector  40  is generated at output O 3  of scalar ALU  34   c , and the fourth component of scalar output vector  40  is generated at output O 0  of scalar ALU  34   d.    
     In addition to the foregoing, each scalar ALU  34   a – 34   d  has its forward output FWD connected to a multiplexer  44 . The output of the multiplexer  44  is connected to the SFU  36  which performs special functions such as 1/x, 1/sqrt, sqrt, log, exp, etc. . . . . The output of the SFU  36  is connected to the SC input of each of the scalar ALUs  34   a – 34   d . As will be explained below, when an instruction to a scalar ALU  34   a – 34   d  cannot be performed by the scalar ALU  34   a – 34   d , the SFU  36  will perform the operation and transfer the result back to the appropriate scalar ALU  34   a – 34   d.    
     The MA input for each scalar ALU  34   a – 34   d  receives address and control signals. The MO output of each scalar ALU  34   a – 34   d  transfers the address and control signals to the next succeeding scalar ALU  34   a – 34   d  with an appropriate delay. As will be further explained below, the delay permits each successive ALU  34   a – 34   d  to process the instruction at the correct cycle in order to support parallel processing of the component vector  33 . Similarly, M bits of common data from memory is inputted into the C input of each scalar ALU  34   a – 34   d  and transferred to a succeeding ALU  34   a – 34   d  by the CO output with the appropriate delay. It can be seen that the address and control signals are distributed sequentially from one scalar ALU  34  to another scalar ALU  34  with the appropriate delay. Furthermore, input data (vector components) are distributed directly to an appropriate input I 0 –I 3  of each scalar ALU  34  thereby providing the required delay for processing in subsequent clock cycles. As can be seen from  FIG. 4 , the scalar processor  42  only has three types of units: the scalar ALUs  34   a – 34   d , the special function unit (SFU)  36 , and the multiplexer  44 , thereby providing a very simple implementation. 
     Referring to  FIGS. 4 ,  5 , and  6 , an example of the instruction cycle timing with the scalar processor  42  is shown. In the first instruction execution cycle ( 1 ), the first scalar ALU  34   a  receives the first component  33   a  at the input I 0  of the first scalar ALU  34   a  and operates on the first component  33   a . The first scalar ALU  34   a  receives control and address data from the microcode unit and receives the common data from memory. Referring to  FIG. 6 , the control and common data are delayed during the instruction execution cycle in the control and common delay registers  68  and  70  and forwarded serially to the to the next scalar ALU  34   b  to be operated on during the next instruction execution cycle. Similarly, the scalar units  34   b ,  34   c , and  34   d  delay and forward the corresponding control and common data to each other sequentially in the same manner. Conversely, referring to  FIG. 4 , the input vector component data  33   b  will be transferred to the input I 1  of the second scalar ALU  34   b . As seen in  FIGS. 4 and 6 , the input vector component data  33   b  will be delayed by register  72  until the next instruction cycle when it will be forwarded from O 1  to input I 0  of the same scalar ALU  34   b . The other scalar ALUs  34   c  and  34   d  will receive this input data to input I 2  and I 3  respectively to provide the required delay for each vector component  33   c  and  33   d.    
     During the second instruction execution cycle ( 2 ), the second scalar ALU  34   b  operates on the second component  33   b  while forwarding control and common data after delay to the third scalar ALU  34   c . At the same time, the output from the first scalar ALU  34   a  and the other vector input vector components  33   c ,  33   d  are delayed by internal delay registers of scalar ALUs  34   a ,  34   c , and  34   d . Similarly, in the third instruction cycle ( 3 ), the third scalar ALU  34   c  operates on the third component  33   c  while the other signals are delayed. In the fourth instruction cycle ( 4 ), the fourth scalar ALU  34   d  operates on the fourth component  33   d  while the other signals are delayed. As can be recognized, each scalar ALU  34   a – 34   d  processes the same instruction on a respective component of the vector, but at a different time. The internal delay registers for the input and output vector components align the output data at the final processing cycle so that a valid result for each executed instruction is provided at every cycle. 
     By delaying the signals during each instruction cycle and staggering the operation of each scalar ALU  34   a – 34   d , it is possible to perform the scalar computation using only one special function unit. Specifically, as seen in  FIG. 5 , the output of each scalar ALU  34   a – 34   d  is bypassed to the input of the multiplexer  44  (i.e., the Fwd output shown in  FIG. 4 ). By bypassing the delay, it is possible for the SFU  36  to perform the function at the appropriate execution instruction cycle. The output of the SFU  36  is inputted into the SC input of each scalar ALU  34   a – 34   d . In this regard, it is possible to use a single SFU  36  in the scalar processor  42 . 
     Referring to  FIG. 6 , the internal structure of each scalar ALU  34   a ,  34   b ,  34   c , and  34   d  is shown. The structure of each scalar ALU  34  is not dependent upon the position of the scalar ALU  34  within the processor  42 . The variation of the port connections define the position of the scalar ALU  34  within the processor  42 . The scalar ALU  34  has a datapath section  46  which includes a 7×4 multiplexer  48 . One of the inputs of the 7×4 multiplexer  48  is port I 0 . The other inputs to the 7×4 multiplexer  48  are common data, and registered data from internal register file  80 , write back register  62 , accumulator  64 , and load register  78 . The datapath section  46  further includes a multiplier  50  connected to two outputs of the multiplexer  48 . Additionally, a 2×1 multiplexer  50  is also connected to one of the outputs of the 7×4 multiplexer  48 . Another input of the 2×1 multiplexer  50  is connected to the output of a multiply accumulator (MACC)  60 . The output of the multiplier  50  and the output of the multiplexer  52  are connected to the inputs of an adder  54 . The output of the adder  54  is connected to the input of the multiply accumulator (MACC)  60  and a carry propagation adder (CPA)  56 . The multiplier  50 , adder  54  and CPA  56  form an arithmetic calculation unit for the ALU  34 . The output of the CPA  56  is connected to the input of a write back (WB) register  62  which generates the output O 0  and also connects it to an input of the 7×4 multiplexer  48  and register file  80 . 
     The datapath section  46  further includes a second 2×1 multiplexer  58  which has an input connected to the output signal of the CPA  56  and the data return signal SC from the special function unit  36 . The output of the multiplexer  58  is fed into an accumulator register ACCxT  64  for accumulating each thread of the process in the register  64 . The output of the accumulator register  64  is connected to one of the inputs of the 7×4 multiplexer  48 . 
     The scalar ALU  34  further includes a register section  66  which contains delay and processing registers. Specifically, the register section  66  has an address and control delay register  68  and a common data delay register  70  which provide the necessary timing delay to the address/control signals, as well as the delay for the common data signals, as previously described for  FIG. 5 . The register section  66 , also includes a load register (LR)  78  which loads results from the 7×4 multiplexer  48 . The register section  66  also has three input delay registers  72 ,  74 , and  76  which delay the input signals I 1 , I 2 , and I 3  as discussed for  FIG. 5 . 
     Referring to  FIG. 6 , in memory section  79 , the scalar ALU  34   a  has a temporary SRAM memory  80  that is an N×M bit  2   r   1   w  SRAM memory which provides read output signals RD 0  and RD 1  to the 7×4 multiplexer  48 . The memory  80  is controlled by control and address module  82  of control section  84  which receives address and control data from port MA and generates the appropriate address and control signals to the multiplexers  48 ,  50  and  52 , as well as the accumulator  64  and load register  78 . 
     An example of the instruction cycle for the present invention will now be described with the aid of  FIG. 7 . The function is the same for a normalized vector difference as was described for  FIG. 2 : 
     
       
         
           
             vector 
             ⁢ 
             
                 
             
             ⁢ 
             Normalized_Difference 
             ⁢ 
             
                 
             
             ⁢ 
             
               ( 
               
                 
                   vector 
                   ⁢ 
                   
                       
                   
                   ⁢ 
                   V1 
                 
                 , 
                 
                   vector 
                   ⁢ 
                   
                       
                   
                   ⁢ 
                   V2 
                 
               
               ) 
             
           
         
       
       
         
           
             
               
                 V1 
                 -&gt; 
                 
                   r0 
                   . 
                   xyzw 
                 
               
               = 
               
                 r0 
                 ⁡ 
                 
                   [ 
                   0 
                   ] 
                 
               
             
             , 
             
               r0 
               ⁡ 
               
                 [ 
                 1 
                 ] 
               
             
             , 
             
               r0 
               ⁡ 
               
                 [ 
                 2 
                 ] 
               
             
             , 
             
               r0 
               ⁡ 
               
                 [ 
                 3 
                 ] 
               
             
           
         
       
       
         
           
             
               
                 V2 
                 -&gt; 
                 
                   r1 
                   . 
                   xyzw 
                 
               
               = 
               
                 r1 
                 ⁡ 
                 
                   [ 
                   0 
                   ] 
                 
               
             
             , 
             
               r1 
               ⁡ 
               
                 [ 
                 1 
                 ] 
               
             
             , 
             
               r1 
               ⁡ 
               
                 [ 
                 2 
                 ] 
               
             
             , 
             
               r1 
               ⁡ 
               
                 [ 
                 3 
                 ] 
               
             
           
         
       
       
         
           
             ( 
             
               x 
               , 
               y 
               , 
               z 
               , 
               
                 w— 
                 ⁢ 
                 
                     
                 
                 ⁢ 
                 components 
                 ⁢ 
                 
                     
                 
                 ⁢ 
                 of 
                 ⁢ 
                 
                     
                 
                 ⁢ 
                 graphics 
                 ⁢ 
                 
                     
                 
                 ⁢ 
                 data 
                 ⁢ 
                 
                     
                 
                 ⁢ 
                 vector 
               
               , 
               
                  
               
               ⁢ 
               
                 
                   r 
                   [ 
                   
                     0 
                     ⁢ 
                     –3 
                   
                   ] 
                 
                 ⁢ 
                 
                     
                 
                 ⁢ 
                 as 
                 ⁢ 
                 
                     
                 
                 ⁢ 
                 separate 
                 ⁢ 
                 
                     
                 
                 ⁢ 
                 scalars 
               
             
             ) 
           
         
       
     
     The corresponding instructions for this function for use with the scalar processing unit  30  are: 
     
       
         
               
               
               
             
               
               
             
               
               
               
             
               
             
           
               
                   
               
             
             
               
                 Repl (j&lt;3) 
                 SUB r2[j],k r0[j], r1[j] 
                 //subtraction of all components 
               
               
                 Repl (j&lt;3) 
                 MAC Null, r2[j], r2[j] 
                 //dot product of all components with 
               
               
                   
                   
                 result in x-component, implements as 
               
               
                   
                   
                 multiply-accumulate 
               
             
          
           
               
                 RSQ ACC, FWD 
                 //reciprocal square root of result in x- 
               
               
                   
                 component forwarded to Special 
               
               
                   
                 Function Unit, paired with MAC 
               
             
          
           
               
                 Repl (j&lt;3) 
                 MUL r2[j], r2[j], ACC 
                 //Scaling all components with the 
               
               
                   
                   
                 RSQ result 
               
             
          
           
               
                 (Repl (j&lt;3) - replication prefix of the same instruction) 
               
               
                   
               
             
          
         
       
     
       FIG. 7  shows the operation that each of the scalar ALUs  34   a – 34   d  perform for the preceding function: vector Normalized_Difference (vector V 1 , vector V 2 ). In the first instruction cycle ( 1 ), the first scalar ALU  34   a  performs the subtraction on the first x component. Then in the second instruction cycle ( 2 ), the scalar ALU  34   a  performs the subtraction on the y component, and in the third instruction cycle ( 3 ), the scalar ALU  34   a  performs the subtraction on the z component. Beginning with the fourth instruction cycle ( 4 ), the scalar ALU  34   a  begins performing the dot product of all of the components by implementing a multiply-accumulate operation. Specifically, in the fourth instruction cycle ( 4 ), the x component is multiplied. In the fifth instruction cycle ( 5 ), the y component is multiplied, and the in the sixth instruction cycle ( 6 ), the z component is multiplied in order to achieve the dot product. Next in the seventh instruction cycle ( 7 ), the dot product is forwarded to the special function unit  36  in order to perform the reciprocal square root (RSQ) thereon. As previously mentioned, the special function unit  36  is operable to perform special functions such as square root, reciprocal square root, log, exp, etc. . . . that cannot be performed by the ALU  34   a . While the special function unit  36  is performing the RSQ operation, the scalar ALU  34   a  remains in an idle state while the result is being obtained and placed in the accumulator (ACC). In the eighth instruction cycle ( 8 ), the result in the accumulator (ACC) is multiplied by the x component in order to scale the result. Similarly, the result in the accumulator is multiplied by the y component in the ninth instruction cycle ( 9 ), and the z component in the tenth instruction cycle ( 10 ). Therefore, the result from the first scalar ALU  34   a  is ready in ten instruction cycles in which the scalar ALU  34   a  is busy in nine instruction cycles and only idle in one instruction cycle. 
     The second, third, and fourth scalar ALUs  34   b ,  34   c , and  34   d  perform the same instructions as the first scalar ALU  34   a  on respective vector components, however delayed. Specifically, as seen in  FIG. 7 , the second scalar ALU  34   b  performs the same instructions as the first scalar ALU  34   a  on the second set of components but delayed one instruction cycle. The instructions operated by the third scalar ALU  34   c  are delayed one instruction cycle from the second scalar ALU  34   b , and the instructions performed by the fourth scalar ALU  34   d  are delayed one instruction cycle from the third scalar ALU  34   c.    
     By delaying each instruction one cycle in a subsequent ALU  34   a – 34   d , it is possible to use only one special function unit  36  in the scalar processor  42 . For example, in instruction cycle seven ( 7 ) for the function shown in  FIG. 7 , the special function unit  36  will process the reciprocal square root (RSQ) for the instruction thread of the first scalar ALU  34   a . In the eighth instruction cycle ( 8 ), the special function unit  36  will process the reciprocal square root for the instruction thread of the second scalar ALU  34   b . For the third scalar ALU  34   c , the reciprocal square root will be processed in instruction cycle nine ( 9 ) and for the fourth scalar ALU  34   d , the reciprocal square root will be processed in instruction cycle ten ( 10 ). 
     The SIMD scalar processing unit  30  can process four sets of graphics data simultaneously with each of the scalar ALUs  34   a – 34   d . As seen in  FIG. 7 , to completely perform the operation, it only takes a total of thirteen instruction cycles (actually ten cycles on average) versus the sixteen instruction cycles for the prior art graphics processor. Furthermore, each scalar ALU  34   a – 34   b  uses only nine instruction cycles for processing and the special function unit  36  processes during one instruction cycle. Accordingly, it takes only a total of ten instruction cycles to obtain the result for one set of graphics data whereas for the prior art processor it took a total of sixteen instruction cycles. Furthermore, the efficiency of the SIMD scalar processor  42  grows with the reduction of vector sizes. For example, for a 2-element vector, the same data can be processed in a total of 8 cycles versus the same 16 cycles required for the prior art architecture. 
     The present invention provides a basic scalar ALU  34   a – 34   d  that can be replicated and controlled in SIMD mode. This provides improved performance scalability and simple basic instructions with a high density of microcode. Furthermore, the present invention provides lower multithreading support hardware overhead than the prior art with compiler simplification and a lower number of instructions. It will be recognized by those of ordinary skill in the art that the scalar processor  42  may be used in other types of processing environments and not just graphics processors. 
     Additional modifications and improvements of the present invention may also be apparent to those of ordinary skill in the art such as having more than four ALUs  34  in order to support larger vectors of any kind. In this respect, the number of ALUs  34  may be varied in order to provide greater efficiency. Thus, the particular combination of parts describes and illustrated herein is intended to represent only a certain embodiment of the present invention, and is not intended to serve as a limitation of alternative devices within the spirit and scope of the invention.