diff --git a/perf/p0.jl b/perf/p0.jl
index 81ce7fd..757d8b3 100644
--- a/perf/p0.jl
+++ b/perf/p0.jl
@@ -1,3 +1,4 @@
+using Random
 using Libtask
 using Turing, DynamicPPL, AdvancedPS
 using BenchmarkTools
@@ -13,7 +14,8 @@ end
 
 
 # Case 1: Sample from the prior.
-m = Turing.Core.TracedModel(gdemo(1.5, 2.), SampleFromPrior(), VarInfo())
+rng = MersenneTwister()
+m = Turing.Core.TracedModel(gdemo(1.5, 2.), SampleFromPrior(), VarInfo(), rng)
 f = m.evaluator[1];
 args = m.evaluator[2:end];
 
@@ -26,7 +28,7 @@ println("Run a tape...")
 @btime t.tf(args...)
 
 # Case 2: SMC sampler
-m = Turing.Core.TracedModel(gdemo(1.5, 2.), Sampler(SMC(50)), VarInfo());
+m = Turing.Core.TracedModel(gdemo(1.5, 2.), Sampler(SMC(50)), VarInfo(), rng)
 f = m.evaluator[1];
 args = m.evaluator[2:end];
 
diff --git a/perf/p2.jl b/perf/p2.jl
index a95b3d3..bd904c3 100644
--- a/perf/p2.jl
+++ b/perf/p2.jl
@@ -41,18 +41,18 @@ using Turing, Test, AbstractMCMC, DynamicPPL, Random, Turing.RandomMeasures, Lib
 end
 
 # Generate some test data.
-Random.seed!(1)
+rng = Random.seed!(1)
 
-data = vcat(randn(10), randn(10) .- 5, randn(10) .+ 10)
+data = vcat(randn(rng, 10), randn(rng, 10) .- 5, randn(rng, 10) .+ 10)
 data .-= mean(data)
 data /= std(data)
 
 # MCMC sampling
-Random.seed!(2)
+Random.seed!(rng, 2)
 iterations = 500
 model_fun = infiniteGMM(data)
 
-m = Turing.Core.TracedModel(model_fun, Sampler(SMC(50)), VarInfo())
+m = Turing.Core.TracedModel(model_fun, Sampler(SMC(50)), VarInfo(), rng)
 f = m.evaluator[1]
 args = m.evaluator[2:end]