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- 1. Weld: A Common Runtime for Data Analytics Shoumik Palkar, James Thomas, Deepak Narayanan, Anil Shanbhag*, Rahul Palamuttam, Holger Pirk*, Malte Schwarzkopf*, Saman Amarasinghe*, Sam Madden*, Matei Zaharia Stanford InfoLab, *MIT CSAIL
- 2. Motivation Modern data apps combine many disjoint processing libraries & functions » SQL, statistics, machine learning, … » E.g. PyData stack
- 3. Motivation Modern data apps combine many disjoint processing libraries & functions » SQL, statistics, machine learning, … » E.g. PyData stack + Great results leveraging work of 1000s of authors – No optimization across these functions
- 4. How Bad is This Problem? Growing gap between memory/processing makes traditional way of combining functions worse data = pandas.parse_csv(string) filtered = pandas.dropna(data) avg = numpy.mean(filtered) parse_csv dropna mean Up to 30x slowdowns in NumPy, Pandas, TensorFlow, etc. compared to optimized C implementation
- 5. Optimizing Across Frameworks machine learning SQL graph algorithms CPU GPU … Common Runtime …
- 6. Optimizing Across Frameworks machine learning SQL graph algorithms CPU GPU … … Weld IR Backends Runtime API Optimizer Weld runtime
- 7. Integrations Partial Prototypes of Pandas, NumPy, TensorFlow and Apache Spark
- 8. Existing Libraries + Weld = 30x TPC-H Logistic RegressionVector Sum 6.8x Faster 4.5x Faster 30x Faster 0 10 20 30 40 TPC-H Q1 TPC-H Q6 Runtime(sec) Spark SQL Weld 0 0.05 0.1 0.15 0.2 Vector sum Runtime(sec) NumPy NumExpr Weld 0.1 1 10 100 1000 1T 12T Runtime(sec,log10) TensorFlow Weld
- 9. Cross libraries + Weld = 290x 0.01 0.1 1 10 100 Runtime(sec,log10) Native Weld, no CLO Weld, CLO Weld, 12 core Pandas + NumPy 290x 31x 0.0 0.5 1.0 1.5 2.0 Runtime(sec) Scala UDF Weld Spark SQL UDF 14x CLO = with cross library optimization
- 10. Integration Effort Small up front cost to enable Weld integration » 500 LoC Easy to port over each operator » 30 LoC each Incrementally Deployable » Weld-enabled ops work with native ops
- 11. Implementation Around 12K lines of Rust » Fast and safe! Perfect for embedding Parallel CPU backend using LLVM GPU support coming soon
- 12. Weld is Open Source! weld.stanford.edu
- 13. Rest of This Talk Runtime API: Enabling cross-library optimization Weld IR: Enabling speedups and automatic parallelization Grizzly: Using Weld to build a Faster Pandas
- 14. Runtime API Uses lazy evaluation to collect work across libraries data = lib1.f1() lib2.map(data, item => lib3.f2(item) ) User Application Weld Runtime Combined IR program Optimized machine code 1101110 0111010 1101111 IR fragments for each function Runtime API f1 map f2 Data in application
- 15. Weld IR Designed to meet three goals: 1. Generality: support diverse workloads and nested calls 2. Ability to express optimizations: e.g., loop fusion, vectorization, loop tiling 3. Explicit parallelism and targeting parallel hardware
- 16. Weld IR: Internals Small IR with only two main constructs. Parallel loops: iterate over a dataset Builders: declarative objects for producing results » E.g. append items to a list, compute a sum » Can be implemented differently on different hardware Captures relational algebra, functional APIs like Spark, linear algebra, and composition thereof
- 17. Examples Implement functional operators using builders def map(data, f): builder = new vecbuilder[int] for x in data: merge(builder, f(x)) result(builder) def reduce(data, zero, func): builder = new merger[zero, func] for x in data: merge(builder, x) result(builder)
- 18. Example Optimization: Fusion squares = map(data, x => x * x) sum = reduce(data, 0, +) bld1 = new vecbuilder[int] bld2 = new merger[0, +] for x in data: merge(bld1, x * x) merge(bld2, x) Loops can be merged into one pass over data
- 19. “Typically lumber along at 1.2-1.8 MPH”“Active, agile, hard to out-run, top speed of 35 MPH”
- 20. Grizzly A subset of Pandas integrated with Weld » Operators ported over including unique, filter, mask » Easy to port more! Same API as native Pandas so zero changes to applications Transparent single-core and multi-core speedups
- 21. Grizzly in action import pandas as pd # Read dataframe from file requests = pd.read_csv(‘filename.csv’) # Fix requests with extra digits requests['Incident Zip'] = requests['Incident Zip'].str.slice(0, 5) # Fix requests with 00000 zipcodes zero_zips = requests['Incident Zip'] == '00000’ requests['Incident Zip'][zero_zips] = np.nan # Display unique incident zips print requests['Incident Zip'].unique() Adapted from http://pandas.pydata.org/pandas-docs/stable/tutorials.html (chapter 7)
- 22. Grizzly in action import pandas as pd, grizzly as gr # Read dataframe from file raw_requests = pd.read_csv(‘filename.txt’) requests = gr.DataFrame(raw_requests) # Fix requests with extra digits requests['Incident Zip'] = requests['Incident Zip'].str.slice(0, 5) # Fix requests with 00000 zipcodes zero_zips = requests['Incident Zip'] == '00000’ requests['Incident Zip'][zero_zips] = np.nan # Display unique incident zips print requests['Incident Zip'].unique().evaluate() Adapted from http://pandas.pydata.org/pandas-docs/stable/tutorials.html (chapter 7)
- 23. Demo
- 24. Conclusion Changing the interface between libraries can speed up data analytics applications by 10-100x on modern hardware Try out Weld for yourself: weld.stanford.edu
Notas del editor
- Modern This is great because anyone can leverage the work of 1000s of authors who have never met each other and are implementing the latest algorithms in each domain. What we’ve lost for this modularity, however, is optimization across these functions. Instead, developers have only focused on making each individual function really fast, but there has been almost no effort in looking for opportunities in optimizing the end-to-end application.
- This is great because anyone can leverage the work of 1000s of authors who have never met each other and are implementing the latest algorithms in each domain. What we’ve lost for this modularity, however, is optimization across these functions. Instead, developers have only focused on making each individual function really fast, but there has been almost no effort in looking for opportunities in optimizing the end-to-end application.
- Is end-to-end optimization in modern data applications something we really need? Growing gap between memory and processing speeds is making the traditional way of combining functions worse In particular, the usual way to compose these libraries is to make function calls into them. To combine different libraries, you take the output of one function call and pass it as the input to another function call in a different library. This means that you keep reading results from memory to do some work and writing them back over and over again. Consider this toy example which reads some CSV and does some computation with NumPy and Pandas. Each of these individual functions is highly optimized and written in C. So even though each individual function is really fast and carefully optimized, a lot of time goes into just reading and writing from memory as opposed to doing any processing. We found up to 30x slowdowns in applications such as NumPy and Pandas and TensorFlow, compared to writing these applications as a single giant C program with one loop that processes the data and that a standard C compiler could optimize. The results were even worse when we combined libraries (for example, using NumPy and TensorFlow in the same application).
- So, how do we solve this problem? Because each function is written as a “black box”, we can’t do any optimizations on existing programs. In addition, data scientists want to continue to mix and match different libraries and frameworks to build their applications, so building one giant “mega system” which can do it all isn’t a practical. The approach we take to solving this is a common runtime which can capture the computation in all the domains people use in their applications in some common format, and then optimize that common format. This common format is similar to how people use libraries like CUDA or OpenCL today to implement accelerated versions of their libraries. The runtime can look at the entire workload and produce one “end-to-end optimized” version of the entire workload by running optimizations like vectorization or loop fusion. We can also target different heterogeneous parallel hardware
- Weld is our implementation of such a runtime. It has four main components First is a runtime API which frameworks and libraries use to register computations with the runtime. Second is an IR to capture the actual work each framework does. An IR is a small programming language. Third is an optimizer which operates over the IR to fuse loops, vectorize code, and so forth Last is a set of backends to generate code for different parallel hardware. First is an IR which to capture parallel work – this is what a single function expresses its computation in. We focus on data parallel workloads because they are predominant in analytics applications - for example, all of Spark's operators are data parallel, and most operators in libraries like TensorFlow and NumPy also fundamentally apply data parallel operations over collections of data.
- Our prototype of Weld has integrations with some real frameworks: Pandas, TensorFlow, Numpy, and Spark. Of these pandas is most complete, open source today
- Weld integration gives us some pretty nice performance boosts TPCH: 400GB total, distributed across 20 nodes, 20GB per node NumPy: Simple sum of 10 million integers in a loop TensorFlow: MNIST again (zero or non-zero), 55000 images. Spark 2.0
- Cross library optimizations can give even crazier speedups. We combined Pandas and NumPy in a simple data cleaning workload. Note Log Scale Dataset size Pandas: 5GB dataset Workload: Filter big cities evaluate a simple linear model of features using features prseent to compute a crime index, and then aggregate into a total crime index. Spark SQL: 10GB TPCH (SF=10) 60 million rows Workload: Read columns, UDF doubles one column, compute sum of pointwise product.
- Weld’s first component is an API to collect work across libraries. The main goal of the API is composability - allowing library writers who have never met and who work in different domains to enable optimizations across their functions. The API uses *lazy evaluation* to build up a large computation, and then compiles the computation into fast machine code and evaluates it using the data in the application. In the example in this picture, a user is calling three Weld-enabled functions, which are stitched together using the runtime API. When the user needs to materialize the result of a computation, the runtime compiles the IR into optimized machine code and produces a result.
- Weld’s runtime produces an IR we have developed, which has three goals. (read goals off slides)
- Apart from some basic operators to do arithmetic, branching, and so forth, Weld's IR has two parallel constructs: a loop which iterates over a dataset in parallel, and an object type called builders for producing results. Builders are declarative types, so they just specify what kind of result they return when you "merge" values into them in parallel. For example, we can perform a reduction by using a loop to pass over some data and merge each element into a builder which takes a commutative merge function (e.g., add if we want to compute a sum). Builders don't specify a specific implementation strategy, so backends can choose how to implement them most efficiently for a given target platform. They also have some limitations that let them be implemented efficiently on parallel hardware. For example, the final result of a builder can only be read out once, after all writes have completed. This allows backends to keep arbitrary, distributed state until an actual result needs to be materialized. The IR can capture workloads from a number of different domains, and more importantly, we've found that it can also capture the compositions of these libraries
- The IR enables optimizations of composed functions. We can express things like common subexpression elimination and vectorization, and because the loops and builders are parallel structures, every loop in Weld can automatically be parallelized as well. In this slide we see a simple optimization which can improve the performance of two composed library calls. One library does a map, and another library does a reduce over the same data. In today's world, each of these functions would be a black box, and you'd have to scan the data twice. However, Weld's runtime API enables composing these into a single block of Weld code, and IR can express fusing these two calls into a single pass over the data.