Fix BenchmarkTools.DEFAULT_PARAMETERS const assignment error in Julia 1.12 (#765)

* Fix BenchmarkTools.DEFAULT_PARAMETERS const assignment error in Julia 1.12

- Remove global modification of BenchmarkTools.DEFAULT_PARAMETERS which
  fails in Julia 1.12+ due to const restriction
- Pass seconds and samples parameters directly to @benchmark macro
- Remove try/finally block that was only needed for parameter restoration
- Fixes issue #744

🤖 Generated with [Claude Code](https://claude.ai/code)

Co-Authored-By: Claude <[email protected]>

* Fix BenchmarkTools.DEFAULT_PARAMETERS const assignment error in Julia 1.12

- Remove global modification of BenchmarkTools.DEFAULT_PARAMETERS which
  fails in Julia 1.12+ due to const restriction
- Use @benchmarkable and BenchmarkTools.run with custom parameters
- Remove try/finally block that was only needed for parameter restoration
- Fixes issue #744

🤖 Generated with [Claude Code](https://claude.ai/code)

Co-Authored-By: Claude <[email protected]>

---------

Co-authored-by: ChrisRackauckas <[email protected]>
Co-authored-by: Claude <[email protected]>

Author: 3 people

lib/LinearSolveAutotune/src/benchmarking.jl

@@ -87,10 +87,9 @@ function benchmark_algorithms(matrix_sizes, algorithms, alg_names, eltypes;
         samples = 5, seconds = 0.5, sizes = [:tiny, :small, :medium, :large],
         check_correctness = true, correctness_tol = 1e0, maxtime = 100.0)
 
-    # Set benchmark parameters
-    old_params = BenchmarkTools.DEFAULT_PARAMETERS
-    BenchmarkTools.DEFAULT_PARAMETERS.seconds = seconds
-    BenchmarkTools.DEFAULT_PARAMETERS.samples = samples
+    # Note: We pass benchmark parameters directly to @benchmark instead of 
+    # modifying BenchmarkTools.DEFAULT_PARAMETERS to avoid const assignment 
+    # errors in Julia 1.12+
 
     # Initialize results DataFrame
     results_data = []
@@ -111,8 +110,7 @@ function benchmark_algorithms(matrix_sizes, algorithms, alg_names, eltypes;
     progress = Progress(total_benchmarks, desc="Benchmarking: ", 
                        barlen=50, showspeed=true)
 
-    try
-        for eltype in eltypes
+    for eltype in eltypes
             # Initialize blocked algorithms dict for this element type
             blocked_algorithms[string(eltype)] = Dict{String, Int}()
 
@@ -234,10 +232,13 @@ function benchmark_algorithms(matrix_sizes, algorithms, alg_names, eltypes;
                                     error_msg = "Insufficient time for benchmarking"
                                 else
                                     # Actual benchmark
-                                    bench = @benchmark solve($prob, $alg) setup=(prob = LinearProblem(
+                                    # Create benchmark with custom parameters
+                                    bench_params = BenchmarkTools.Parameters(;seconds=seconds, samples=samples)
+                                    b = @benchmarkable solve($prob, $alg) setup=(prob = LinearProblem(
                                         copy($A), copy($b);
                                         u0 = copy($u0),
                                         alias = LinearAliasSpecifier(alias_A = true, alias_b = true)))
+                                    bench = BenchmarkTools.run(b, bench_params)
 
                                     # Calculate GFLOPs
                                     min_time_sec = minimum(bench.times) / 1e9
@@ -269,11 +270,6 @@ function benchmark_algorithms(matrix_sizes, algorithms, alg_names, eltypes;
                     ProgressMeter.next!(progress)
                 end
             end
-        end
-
-    finally
-        # Restore original benchmark parameters
-        BenchmarkTools.DEFAULT_PARAMETERS = old_params
     end
 
     return DataFrame(results_data)