Enhancing GPU for Scientific Computing

Some thoughts

Outline

Motivation Related work BLAS Library Execution Model Benchmarks Recommendations

Motivation

GPU Computing• Vector and Fragment Processor

streaming (super)-computers

• enormous performance!

• ATI 9700, NV30

They have become programmable Emerging application areas

• Numerical Sim.[Schroder’03], Sorting, Genomics, etc.

• Goal: Scientific Computing

Motivation

Most software built from small-efficient parts

Scientific apps built on top of s/w library routines

Harnessing GPU resources • Arithmetic Intensive

• Data parallel

BLAS Library

Related work

Using non-programmable GPUs [Erik’01] prog. vertex engine for lighting/morphing [Oskin’02] vector processing using VP [Ian’03] stream processing using FP Problems :

• Monolithic Big Programs

• One of VP or FP

• CPU – Passive Mode

• No Cascading Loop-backs (Parallelism, Setup Times)

BLAS Library

BLAS (Basic Linear Algebra Subprograms)• Building blocks for vector and matrix operations

• development of highly efficient linear algebra software • LINPACK and LAPACK

Operations• Scalar – Vector

• Vector – Vector

• Vector – Matrix

• Matrix – Matrix

Mapping

Operation processor

CPU/FP - All ops

VP - no memory access

Restricted data-flows• CPU FP

• VP CPU

CPU

VP

FP

Non-matrix ops

All operations

All operations

CPU VP FP CPU

Execution graphExecution graph

Vector Scalar Add Operation

1. In this example, a Vector of length n is segmented into m other vectors of length 4 in the CPU function vsAdd.

2. The vertex program vsAdd.cg is loaded onto the vertex processor and the scalar value is passed as a parameter.

3. Subsequently, CPU function vsAdd will stream the set of m vectors onto the CPU as openGL primitive points. Our vertex program, vsAdd.cg will add the scalar value to all fields in the m vertices.

4. Consequently, these vertices will proceed to the fragment processor and written onto the framebuffer memory.

5. The CPU function vsADD continues to read the color values off each pixel representation of the vertices. These color values contain result of a Vector Scalar add.

6. Lastly the CPU function concatenates the sequence of color values into a vector of length n as result.

vAddCPU

[vAdd.cg]Vertex Processor

vAddCPU

(Vectors, Vectors)

(Vectors)

[Vertex]m (GL_POINTS)

[Texture Color values]m

[None]Fragment Processor

[Vertex]m

G

P

U

Texture Mem PBuffer

vAdd.cg

TextureDatam

vAddCPU

[None]Vertex Processor

vAddCPU

GL_QUAD [Vector4]m

(Vectors)

[Vertex4]m GL_QUAD

[Texture Color values]m

[vAdd.cg]Fragment Processor

[Vertex4]m

Execution graphExecution graph

Vector Vector Add Operation1. In this example, 2 vectors of length s are

transformed into texture data in the CPU function vAdd.

2. The vertex program vAdd.cg, and texture data are loaded onto the fragment processor GPU memory respectively.

3. Subsequently, CPU function vAdd will draw a quadrilateral primitive having s pixels.

4. The vertex processor does nothing and passes on the vertices to the rasterizer to process into pixel representation.

5. The rasterizer creates the s pixels for fragment processing.

6. For each pixel, our fragment processor will lookup the values from both textures and determine the color value of each pixel. These pixels are written onto the Pbuffer memory.

7. The CPU function vADD continues to read the color values off each pixel representation of the vertices. These color values contain result of a Vector Vector add.

8. The output in Pbuffer is then converted into a texture entry.

9. Lastly the CPU function reads the texture entry and concatenates the sequence of color values into a vector of length s as result.

G

P

UTextureData1m

TextureData2mTexture Mem PBuffer

vAdd.cg

TextureData3m

vAddCPU

[None]Vertex Processor

vAddCPU

(Vectors)

[Vertex4]m

[Texture Color values]m

[vAdd.cg]Fragment Processor

[Vertex4]m

G

P

U

TextureData4m

Texture Mem PBuffer

TextureData3m

Execution graphExecution graph

2 Vector Vector Add Operations

1. In this example, we perform 2 separate vector vector add operations.

2. The 1st operation proceeds as described earlier in our vector vector add operation.

3. The output of the 1st operation is used as input for the 2nd operation.

4. Since it’s the same operation, we do not load a new Vertex or Fragment program. However we proceed to load a new texture data.

5. The 2nd operation proceeds as normal.

6. Lastly the CPU function concatenates the sequence of color values into a vector of length s as result.

GL_QUAD [Vectex4]m

Performance Issues

Representation inefficiency• Memory

• Data stored both in CPU and GPU

• Communication costs• Loading data onto GPU

• Reading data from GPU

Execution inefficiencies• Computation setup overhead

• Remodeling CPU data for GPU

• Problem execution time• Rendering

• Texture lookups

0%

20%

40%

60%

80%

100%

1 2 4 8 16 32

Operation breakdown

Fixed-Point processing

Readback

Compute

Setup

Load

Fixed-Point (Vector-Vector Add)

0

2000

4000

6000

8000

10000

12000

14000

16000

1 2 4 8 16 32

Number of Operations

Tim

e in

Mill

isec

onds

Resident Non-Resident

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

1 2 4 8 16 32

Operation Breakdown FP32 processing

Readback

Compute

Setup

Load

FP32 Performance (Vector-Vector Add)

0

5000

10000

15000

20000

25000

30000

35000

40000

1 2 4 8 16 32

Number of cascading operations

Tim

e in

Mill

isec

onds

Resident Non-Resident

Observations

Fixed-point operations are much faster than FP16/FP32 operations

FP16/FP32 operations have similar performance

VP is slower than FP• Operation mappings involving both VP and

FP result in inefficient pipeline

Observations

Simple operations perform better on CPU Best to design whole algorithm as single

VP/FP program• Memory cost for storing intermediate results

• Execution cost ?

• More textures result in decreased performance

Bug Reports Filed!

Incorrect dump of floating point values after render to texture [NVIDIA confirmed]

cgSetcolor parameter does not update alpha values [Awaiting reply]

Recommendations (3D Graphics Hackers)

Load important data into Video memory

Maximum use of Fixed-point Pipeline

Code optimization important (Instr., Memory)

Upgrade your video card drivers (must!)• Hacking graphics hardware is a *real* pain!

Recommendations (Cg) Pointer meaningful for numerical computing

Texture fetch instructions (add. Offsets)

Accumulation registers (sum)

Preserving State across multiple calls

Introduce stack mechanisms

Introduce bit wise operators

Recommendations (Hardware)

Allow GPU to read/write from CPU memory VP and FP as 1st class processors on GPU

• Similar cores and instruction sets

• Allow full parallelism Allow CPU to read/write all registers in GPU

processors Introduce a stack Introduce bit wise operators

Deliverables!

A draft subset of the BLAS library

Architecture Insights (issues/constraints)

NV30 Improvements (Bug reports)

Technical Write-up

The End