Memory 如何分析Julia内存分配和代码覆盖率结果
我正在编写一个使用Gibbs抽样的贝叶斯推理包。由于这些方法通常计算量很大，我非常关心代码的性能。事实上，速度是我从Python转到Julia的原因 实现后，我使用和Memory 如何分析Julia内存分配和代码覆盖率结果,memory,julia,Memory,Julia,我正在编写一个使用Gibbs抽样的贝叶斯推理包。由于这些方法通常计算量很大，我非常关心代码的性能。事实上，速度是我从Python转到Julia的原因 实现后，我使用和--track allocation=user命令行选项分析了代码 以下是报道结果 - #= - DPM - - Dirichlet Process Mixture Models - - 25/08/2015 - Ad
--track allocation=user - #=
- DPM
-
- Dirichlet Process Mixture Models
-
- 25/08/2015
- Adham Beyki, odinay@gmail.com
-
- =#
-
- type DPM{T}
- bayesian_component::T
- K::Int64
- aa::Float64
- a1::Float64
- a2::Float64
- K_hist::Vector{Int64}
- K_zz_dict::Dict{Int64, Vector{Int64}}
-
- DPM{T}(c::T, K::Int64, aa::Float64, a1::Float64, a2::Float64) = new(c, K, aa, a1, a2,
- Int64[], (Int64 => Vector{Int64})[])
- end
1 DPM{T}(c::T, K::Int64, aa::Real, a1::Real, a2::Real) = DPM{typeof(c)}(c, K, convert(Float64, aa),
- convert(Float64, a1), convert(Float64, a2))
-
- function Base.show(io::IO, dpm::DPM)
- println(io, "Dirichlet Mixture Model with $(dpm.K) $(typeof(dpm.bayesian_component)) components")
- end
-
- function initialize_gibbs_sampler!(dpm::DPM, zz::Vector{Int64})
- # populates the cluster labels randomly
1 zz[:] = rand(1:dpm.K, length(zz))
- end
-
- function DPM_sample_hyperparam(aa::Float64, a1::Float64, a2::Float64, K::Int64, NN::Int64, iters::Int64)
-
- # resampling concentration parameter based on Escobar and West 1995
352 for n = 1:iters
3504 eta = rand(Distributions.Beta(aa+1, NN))
3504 rr = (a1+K-1) / (NN*(a2-log(NN)))
3504 pi_eta = rr / (1+rr)
-
3504 if rand() < pi_eta
0 aa = rand(Distributions.Gamma(a1+K, 1/(a2-log(eta))))
- else
3504 aa = rand(Distributions.Gamma(a1+K-1, 1/(a2-log(eta))))
- end
- end
352 aa
- end
-
- function DPM_sample_pp{T1, T2}(
- bayesian_components::Vector{T1},
- xx::T2,
- nn::Vector{Float64},
- pp::Vector{Float64},
- aa::Float64)
-
1760000 K = length(nn)
1760000 @inbounds for kk = 1:K
11384379 pp[kk] = log(nn[kk]) + logpredictive(bayesian_components[kk], xx)
- end
1760000 pp[K+1] = log(aa) + logpredictive(bayesian_components[K+1], xx)
1760000 normalize_pp!(pp, K+1)
1760000 return sample(pp[1:K+1])
- end
-
-
- function collapsed_gibbs_sampler!{T1, T2}(
- dpm::DPM{T1},
- xx::Vector{T2},
- zz::Vector{Int64},
- n_burnins::Int64, n_lags::Int64, n_samples::Int64, n_internals::Int64; max_clusters::Int64=100)
-
-
2 NN = length(xx) # number of data points
2 nn = zeros(Float64, dpm.K) # count array
2 n_iterations = n_burnins + (n_samples)*(n_lags+1)
2 bayesian_components = [deepcopy(dpm.bayesian_component) for k = 1:dpm.K+1]
2 dpm.K_hist = zeros(Int64, n_iterations)
2 pp = zeros(Float64, max_clusters)
-
2 tic()
2 for ii = 1:NN
10000 kk = zz[ii]
10000 additem(bayesian_components[kk], xx[ii])
10000 nn[kk] += 1
- end
2 dpm.K_hist[1] = dpm.K
2 elapsed_time = toq()
-
2 for iteration = 1:n_iterations
-
352 println("iteration: $iteration, KK: $(dpm.K), KK mode: $(indmax(hist(dpm.K_hist,
- 0.5:maximum(dpm.K_hist)+0.5)[2])), elapsed time: $elapsed_time")
-
352 tic()
352 @inbounds for ii = 1:NN
1760000 kk = zz[ii]
1760000 nn[kk] -= 1
1760000 delitem(bayesian_components[kk], xx[ii])
-
- # remove the cluster if empty
1760000 if nn[kk] == 0
166 println("\tcomponent $kk has become inactive")
166 splice!(nn, kk)
166 splice!(bayesian_components, kk)
166 dpm.K -= 1
-
- # shifting the labels one cluster back
830166 idx = find(x -> x>kk, zz)
166 zz[idx] -= 1
- end
-
1760000 kk = DPM_sample_pp(bayesian_components, xx[ii], nn, pp, dpm.aa)
-
1760000 if kk == dpm.K+1
171 println("\tcomponent $kk activated.")
171 push!(bayesian_components, deepcopy(dpm.bayesian_component))
171 push!(nn, 0)
171 dpm.K += 1
- end
-
1760000 zz[ii] = kk
1760000 nn[kk] += 1
1760000 additem(bayesian_components[kk], xx[ii])
- end
-
352 dpm.aa = DPM_sample_hyperparam(dpm.aa, dpm.a1, dpm.a2, dpm.K, NN, n_internals)
352 dpm.K_hist[iteration] = dpm.K
352 dpm.K_zz_dict[dpm.K] = deepcopy(zz)
352 elapsed_time = toq()
- end
- end
-
- function truncated_gibbs_sampler{T1, T2}(dpm::DPM{T1}, xx::Vector{T2}, zz::Vector{Int64},
- n_burnins::Int64, n_lags::Int64, n_samples::Int64, n_internals::Int64, K_truncation::Int64)
-
- NN = length(xx) # number of data points
- nn = zeros(Int64, K_truncation) # count array
- bayesian_components = [deepcopy(dpm.bayesian_component) for k = 1:K_truncation]
- n_iterations = n_burnins + (n_samples)*(n_lags+1)
- dpm.K_hist = zeros(Int64, n_iterations)
- states = (ASCIIString => Int64)[]
- n_states = 0
-
- tic()
- for ii = 1:NN
- kk = zz[ii]
- additem(bayesian_components[kk], xx[ii])
- nn[kk] += 1
- end
- dpm.K_hist[1] = dpm.K
-
- # constructing the sticks
- beta_VV = rand(Distributions.Beta(1.0, dpm.aa), K_truncation)
- beta_VV[end] = 1.0
- π = ones(Float64, K_truncation)
- π[2:end] = 1 - beta_VV[1:K_truncation-1]
- π = log(beta_VV) + log(cumprod(π))
-
- elapsed_time = toq()
-
- for iteration = 1:n_iterations
-
- println("iteration: $iteration, # active components: $(length(findn(nn)[1])), mode: $(indmax(hist(dpm.K_hist,
- 0.5:maximum(dpm.K_hist)+0.5)[2])), elapsed time: $elapsed_time \n", nn)
-
- tic()
- for ii = 1:NN
- kk = zz[ii]
- nn[kk] -= 1
- delitem(bayesian_components[kk], xx[ii])
-
- # resampling label
- pp = zeros(Float64, K_truncation)
- for kk = 1:K_truncation
- pp[kk] = π[kk] + logpredictive(bayesian_components[kk], xx[ii])
- end
- pp = exp(pp - maximum(pp))
- pp /= sum(pp)
-
- # sample from pp
- kk = sampleindex(pp)
- zz[ii] = kk
- nn[kk] += 1
- additem(bayesian_components[kk], xx[ii])
-
- for kk = 1:K_truncation-1
- gamma1 = 1 + nn[kk]
- gamma2 = dpm.aa + sum(nn[kk+1:end])
- beta_VV[kk] = rand(Distributions.Beta(gamma1, gamma2))
- end
- beta_VV[end] = 1.0
- π = ones(Float64, K_truncation)
- π[2:end] = 1 - beta_VV[1:K_truncation-1]
- π = log(beta_VV) + log(cumprod(π))
-
- # resampling concentration parameter based on Escobar and West 1995
- for internal_iters = 1:n_internals
- eta = rand(Distributions.Beta(dpm.aa+1, NN))
- rr = (dpm.a1+dpm.K-1) / (NN*(dpm.a2-log(NN)))
- pi_eta = rr / (1+rr)
-
- if rand() < pi_eta
- dpm.aa = rand(Distributions.Gamma(dpm.a1+dpm.K, 1/(dpm.a2-log(eta))))
- else
- dpm.aa = rand(Distributions.Gamma(dpm.a1+dpm.K-1, 1/(dpm.a2-log(eta))))
- end
- end
- end
-
- nn_string = nn2string(nn)
- if !haskey(states, nn_string)
- n_states += 1
- states[nn_string] = n_states
- end
- dpm.K_hist[iteration] = states[nn_string]
- dpm.K_zz_dict[states[nn_string]] = deepcopy(zz)
- elapsed_time = toq()
- end
- return states
- end
-
-
- function posterior{T1, T2}(dpm::DPM{T1}, xx::Vector{T2}, K::Int64, K_truncation::Int64=0)
2 n_components = 0
1 if K_truncation == 0
1 n_components = K
- else
0 n_components = K_truncation
- end
-
1 bayesian_components = [deepcopy(dpm.bayesian_component) for kk=1:n_components]
1 zz = dpm.K_zz_dict[K]
-
1 NN = length(xx)
1 nn = zeros(Int64, n_components)
-
1 for ii = 1:NN
5000 kk = zz[ii]
5000 additem(bayesian_components[kk], xx[ii])
5000 nn[kk] += 1
- end
-
1 return([posterior(bayesian_components[kk]) for kk=1:n_components], nn)
- end
-
答案是简单提到的，“在用户设置下，任何直接从REPL调用的函数的第一行都会由于REPL代码本身中发生的事件而显示分配。”也可能是相关的：“更重要的是，JIT编译也会增加分配计数，因为Julia的编译器大部分都是用Julia编写的（编译通常需要内存分配）。建议执行所有要分析的命令，然后调用Profile.clear_malloc_data（）重置所有分配计数器，从而强制编译
一句话：第一行被指责负责其他地方发生的分配，因为这是第一行再次开始报告分配。NN的值是多少？我建议在分配内存之前，首先使用探查器来识别慢部分。我使用了探查器和
NN=length（xx）xx：：Vector{Float64} - #=
- DPM
-
- Dirichlet Process Mixture Models
-
- 25/08/2015
- Adham Beyki, odinay@gmail.com
-
- =#
-
- type DPM{T}
- bayesian_component::T
- K::Int64
- aa::Float64
- a1::Float64
- a2::Float64
- K_hist::Vector{Int64}
- K_zz_dict::Dict{Int64, Vector{Int64}}
-
- DPM{T}(c::T, K::Int64, aa::Float64, a1::Float64, a2::Float64) = new(c, K, aa, a1, a2,
- Int64[], (Int64 => Vector{Int64})[])
- end
0 DPM{T}(c::T, K::Int64, aa::Real, a1::Real, a2::Real) = DPM{typeof(c)}(c, K, convert(Float64, aa),
- convert(Float64, a1), convert(Float64, a2))
-
- function Base.show(io::IO, dpm::DPM)
- println(io, "Dirichlet Mixture Model with $(dpm.K) $(typeof(dpm.bayesian_component)) components")
- end
-
- function initialize_gibbs_sampler!(dpm::DPM, zz::Vector{Int64})
- # populates the cluster labels randomly
0 zz[:] = rand(1:dpm.K, length(zz))
- end
-
- function DPM_sample_hyperparam(aa::Float64, a1::Float64, a2::Float64, K::Int64, NN::Int64, iters::Int64)
-
- # resampling concentration parameter based on Escobar and West 1995
0 for n = 1:iters
0 eta = rand(Distributions.Beta(aa+1, NN))
0 rr = (a1+K-1) / (NN*(a2-log(NN)))
0 pi_eta = rr / (1+rr)
-
0 if rand() < pi_eta
0 aa = rand(Distributions.Gamma(a1+K, 1/(a2-log(eta))))
- else
0 aa = rand(Distributions.Gamma(a1+K-1, 1/(a2-log(eta))))
- end
- end
0 aa
- end
-
- function DPM_sample_pp{T1, T2}(
- bayesian_components::Vector{T1},
- xx::T2,
- nn::Vector{Float64},
- pp::Vector{Float64},
- aa::Float64)
-
0 K = length(nn)
0 @inbounds for kk = 1:K
0 pp[kk] = log(nn[kk]) + logpredictive(bayesian_components[kk], xx)
- end
0 pp[K+1] = log(aa) + logpredictive(bayesian_components[K+1], xx)
0 normalize_pp!(pp, K+1)
0 return sample(pp[1:K+1])
- end
-
-
- function collapsed_gibbs_sampler!{T1, T2}(
- dpm::DPM{T1},
- xx::Vector{T2},
- zz::Vector{Int64},
- n_burnins::Int64, n_lags::Int64, n_samples::Int64, n_internals::Int64; max_clusters::Int64=100)
-
-
191688 NN = length(xx) # number of data points
96 nn = zeros(Float64, dpm.K) # count array
0 n_iterations = n_burnins + (n_samples)*(n_lags+1)
384 bayesian_components = [deepcopy(dpm.bayesian_component) for k = 1:dpm.K+1]
2864 dpm.K_hist = zeros(Int64, n_iterations)
176 pp = zeros(Float64, max_clusters)
-
48 tic()
0 for ii = 1:NN
0 kk = zz[ii]
0 additem(bayesian_components[kk], xx[ii])
0 nn[kk] += 1
- end
0 dpm.K_hist[1] = dpm.K
0 elapsed_time = toq()
-
0 for iteration = 1:n_iterations
-
5329296 println("iteration: $iteration, KK: $(dpm.K), KK mode: $(indmax(hist(dpm.K_hist,
- 0.5:maximum(dpm.K_hist)+0.5)[2])), elapsed time: $elapsed_time")
-
16800 tic()
28000000 @inbounds for ii = 1:NN
0 kk = zz[ii]
0 nn[kk] -= 1
0 delitem(bayesian_components[kk], xx[ii])
-
- # remove the cluster if empty
0 if nn[kk] == 0
161880 println("\tcomponent $kk has become inactive")
0 splice!(nn, kk)
0 splice!(bayesian_components, kk)
0 dpm.K -= 1
-
- # shifting the labels one cluster back
69032 idx = find(x -> x>kk, zz)
42944 zz[idx] -= 1
- end
-
0 kk = DPM_sample_pp(bayesian_components, xx[ii], nn, pp, dpm.aa)
-
0 if kk == dpm.K+1
158976 println("\tcomponent $kk activated.")
14144 push!(bayesian_components, deepcopy(dpm.bayesian_component))
4872 push!(nn, 0)
0 dpm.K += 1
- end
-
0 zz[ii] = kk
0 nn[kk] += 1
0 additem(bayesian_components[kk], xx[ii])
- end
-
0 dpm.aa = DPM_sample_hyperparam(dpm.aa, dpm.a1, dpm.a2, dpm.K, NN, n_internals)
0 dpm.K_hist[iteration] = dpm.K
14140000 dpm.K_zz_dict[dpm.K] = deepcopy(zz)
0 elapsed_time = toq()
- end
- end
-
- function truncated_gibbs_sampler{T1, T2}(dpm::DPM{T1}, xx::Vector{T2}, zz::Vector{Int64},
- n_burnins::Int64, n_lags::Int64, n_samples::Int64, n_internals::Int64, K_truncation::Int64)
-
- NN = length(xx) # number of data points
- nn = zeros(Int64, K_truncation) # count array
- bayesian_components = [deepcopy(dpm.bayesian_component) for k = 1:K_truncation]
- n_iterations = n_burnins + (n_samples)*(n_lags+1)
- dpm.K_hist = zeros(Int64, n_iterations)
- states = (ASCIIString => Int64)[]
- n_states = 0
-
- tic()
- for ii = 1:NN
- kk = zz[ii]
- additem(bayesian_components[kk], xx[ii])
- nn[kk] += 1
- end
- dpm.K_hist[1] = dpm.K
-
- # constructing the sticks
- beta_VV = rand(Distributions.Beta(1.0, dpm.aa), K_truncation)
- beta_VV[end] = 1.0
- π = ones(Float64, K_truncation)
- π[2:end] = 1 - beta_VV[1:K_truncation-1]
- π = log(beta_VV) + log(cumprod(π))
-
- elapsed_time = toq()
-
- for iteration = 1:n_iterations
-
- println("iteration: $iteration, # active components: $(length(findn(nn)[1])), mode: $(indmax(hist(dpm.K_hist,
- 0.5:maximum(dpm.K_hist)+0.5)[2])), elapsed time: $elapsed_time \n", nn)
-
- tic()
- for ii = 1:NN
- kk = zz[ii]
- nn[kk] -= 1
- delitem(bayesian_components[kk], xx[ii])
-
- # resampling label
- pp = zeros(Float64, K_truncation)
- for kk = 1:K_truncation
- pp[kk] = π[kk] + logpredictive(bayesian_components[kk], xx[ii])
- end
- pp = exp(pp - maximum(pp))
- pp /= sum(pp)
-
- # sample from pp
- kk = sampleindex(pp)
- zz[ii] = kk
- nn[kk] += 1
- additem(bayesian_components[kk], xx[ii])
-
- for kk = 1:K_truncation-1
- gamma1 = 1 + nn[kk]
- gamma2 = dpm.aa + sum(nn[kk+1:end])
- beta_VV[kk] = rand(Distributions.Beta(gamma1, gamma2))
- end
- beta_VV[end] = 1.0
- π = ones(Float64, K_truncation)
- π[2:end] = 1 - beta_VV[1:K_truncation-1]
- π = log(beta_VV) + log(cumprod(π))
-
- # resampling concentration parameter based on Escobar and West 1995
- for internal_iters = 1:n_internals
- eta = rand(Distributions.Beta(dpm.aa+1, NN))
- rr = (dpm.a1+dpm.K-1) / (NN*(dpm.a2-log(NN)))
- pi_eta = rr / (1+rr)
-
- if rand() < pi_eta
- dpm.aa = rand(Distributions.Gamma(dpm.a1+dpm.K, 1/(dpm.a2-log(eta))))
- else
- dpm.aa = rand(Distributions.Gamma(dpm.a1+dpm.K-1, 1/(dpm.a2-log(eta))))
- end
- end
- end
-
- nn_string = nn2string(nn)
- if !haskey(states, nn_string)
- n_states += 1
- states[nn_string] = n_states
- end
- dpm.K_hist[iteration] = states[nn_string]
- dpm.K_zz_dict[states[nn_string]] = deepcopy(zz)
- elapsed_time = toq()
- end
- return states
- end
-
-
- function posterior{T1, T2}(dpm::DPM{T1}, xx::Vector{T2}, K::Int64, K_truncation::Int64=0)
0 n_components = 0
0 if K_truncation == 0
0 n_components = K
- else
0 n_components = K_truncation
- end
-
0 bayesian_components = [deepcopy(dpm.bayesian_component) for kk=1:n_components]
0 zz = dpm.K_zz_dict[K]
-
0 NN = length(xx)
0 nn = zeros(Int64, n_components)
-
0 for ii = 1:NN
0 kk = zz[ii]
0 additem(bayesian_components[kk], xx[ii])
0 nn[kk] += 1
- end
-
0 return([posterior(bayesian_components[kk]) for kk=1:n_components], nn)
- end
-
2 NN = length(xx) # number of data points
191688 NN = length(xx) # number of data points
352 @inbounds for ii = 1:NN
28000000 @inbounds for ii = 1:NN