[Enhancement] Simplify GEMM example with direct kernel compilation (#191)

* Optimize CMake build process with dynamic job count calculation

- Modify build_csrc function to use 90% of available CPU cores
- Ensure at least one job is used during compilation
- Improve build performance by dynamically adjusting parallel job count

* Optimize build_csrc function with multiprocessing module

- Replace os.cpu_count() with multiprocessing.cpu_count()
- Maintain existing 90% CPU utilization logic
- Improve CPU core count calculation for build process

* Add dynamic shape support with out_idx in Cython JIT kernel compilation

- Implement `run_cython_dynamic_shape_with_out_idx` function in test_tilelang_jit_gemm_cython.py
- Update Cython wrapper to handle dynamic symbolic shapes during tensor allocation
- Add support for resolving dynamic shape dimensions using input tensor references
- Enhance flexibility of JIT kernel compilation with symbolic shape handling

* Enhance error reporting for dynamic symbolic shape resolution in Cython JIT kernel

- Add detailed error message when a dynamic symbolic dimension is not found in dynamic_symbolic_map
- Improve debugging by providing context about missing symbolic dimensions
- Maintain existing dynamic shape resolution logic

* Fix Copy operation handling for scalar and multi-dimensional tensors

- Add special handling for scalar tensor copy operations
- Enhance error reporting in MakeIndices method with more detailed diagnostic information
- Improve SIMT loop generation to support zero-dimensional tensors
- Add explicit check and handling for scalar tensor scenarios

* Refactor Copy operation code formatting and improve readability

- Improve code formatting in MakeIndices and MakeSIMTLoop methods
- Add line breaks to enhance readability of complex ICHECK statements
- Simplify code structure in scalar tensor handling
- Remove unnecessary whitespace and improve code alignment

* Simplify GEMM example with direct kernel compilation

- Update copyright header to Tile-AI Corporation
- Remove Profiler import and usage
- Replace tilelang.lower() with tilelang.compile()
- Simplify kernel execution workflow
- Update kernel source retrieval method

examples/gemm/example_gemm.py

-# Copyright (c) Microsoft Corporation.
-# Licensed under the MIT License.
-
 import tilelang
-from tilelang import Profiler
 import tilelang.language as T
 
 
@@ -34,16 +30,14 @@ func = matmul(1024, 1024, 1024, 128, 128, 32)
 
 print(func)
 
-rt_mod, params = tilelang.lower(func)
-
-profiler = Profiler(rt_mod, params, result_idx=[2])
+kernel = tilelang.compile(func, out_idx=-1)
 
 import torch
 
 a = torch.randn(1024, 1024).cuda().half()
 b = torch.randn(1024, 1024).cuda().half()
 
-c = profiler(a, b)
+c = kernel(a, b)
 
 ref_c = a @ b
 
@@ -53,4 +47,4 @@ print(ref_c)
 torch.testing.assert_close(c, ref_c, rtol=1e-2, atol=1e-2)
 
 # Get CUDA Source
-print(rt_mod.imported_modules[0].get_source())
+print(kernel.get_kernel_source())