Repost: Code Smells ... Is concurrency natural?

Writing parallel code is not considered easy, but it can be a natural approach to some problems for novices.  When a beginner wants something to happen twice concurrently, the reasonable thing would be to do what works once, a second time.  Instead, this may conflict with other constructs of the language, such as main() or having to create threads.  See more here.

Talk: Concurrent Data Structures for Non-Volatile Memory

Today, Michal Friedman, gave a talk on Concurrent Data Structures for Non-Volatile Memory.

Future systems will contain non-volatile memory.  This is memory that exhibits normal DRAM characteristics, but can maintain its contents even across power failures.  In current systems, caches update memory on either evictions and flushes.  Flushes, however, impose overhead due to the memory access time and overriding the write-back nature of most caches.

Linearizability is one definition for concurrency governing the observation of the operations.  This can be extended to durable linearizability being on a durable system, such that data is flushed before global visibility (initialization), flush prior operations (dependence), and persist operations before they complete (completion).  But a further extension is required to know when a sequence of operations are complete, beyond just taking snapshots of the memory state.

Relaxed, durable, and log versions of lock-free queue that extend Michael and Scott's baseline queue implementation.  Each version provides stronger guarantees: relaxed are the existing augmented with a sync operation to snapshot state, durable preserves the data structure across failures, log identifies the specific state.  The main guarantee is that the data structure will be consistent for any set of thread crashes, which is stronger than the lock-free guarantee.

We do this by extending the prior lock-free versions that include memory flushes of key state, and that later update which see volatile state will flush that state before completing their operations.  This meets the durable linearizability.  And can be extended by also have a log of operations that are updated and maintained before the operations themselves execute.  These logs are per-thread, so as to be unordered and to be individually stateful.

The relaxed version implements sync by creating a special object that indicates a snapshot is occurring.  If other concurrent operations find this object, they take over the snapshot and continue persisting the state before completing its own operation.  Thus a snapshot does not block other operations, but still occurs at that point in the sequence of operations.

Based on performance measurements, the relaxed performs similar to the baseline implementation, while the durable and log-based implementations run slower than the relaxed but with similar performance.

Finally, TSO provides us a guarantee that the stores will reach the cache line in a desired order and not require flushing between writes.

Conference Attendance - SIGCSE 2019 - Day 2.5

Continuing at SIGCSE, here are several more paper talks that I attended on Friday.  Most of the value at SIGCSE comes from the friendly conversations with other attendees.  From 5-11p, I was in the hotel lobby talking with faculty and students.  Discussing research ideas, telling our stories from teaching, and generally renewing friendships within the field.

On the Effect of Question Ordering on Performance and Confidence in Computer Science Examinations
On the exams, students were offered a bonus if they could predict their score by within 10%.  Does the order of questions (easy -> hard, or hard -> easy) have any impact on their estimated or actual performance on an exam.  Students overpredicted by over 10% on the exams.  As a whole, the hard to easy students did worse, but this result was not statistically significant.  A small improvement is gained for women when the exams start with the hardest problem.

I wonder about whether students were biased in their prediction based on the reward.  Ultimately, the authors gave the reward to all students regardless of the quality of their prediction.

The Relationship between Prerequisite Proficiency and Student Performance in an Upper-Division Computing Course

We have prerequisites to ensure that students are prepared for the later course, an upper-level data structures class.  Students started on average with 57% of expected prerequisite knowledge, and will finish the course with an improvement of 8% on this knowledge.  There is a correlation between prerequisite score and their final score.  With several prerequisites, some knowledge concepts has greater correlation than others.  Assembly is a surprising example of a concept that relates.  Students benefit from intervention that addresses these deficiencies early in the term.

Afterward, we discussed that this work did not explore what prerequisite knowledge weakly correlated with student learning.  How might we better understand what prerequisites actually support the learning in a course?  Furthermore, can we better understand the general background of students in the course, such as class standing or general experience?

Visualizing Classic Synchronization Problems

For three classic synchronization problems: dining philosophers, bounded producer-consumer, and readers and writers.  Each one has a window displaying the operations, as well as multiple algorithmic strategies.  With these visualizations, do students learn better and also find them more engaging than reading about the problems in the textbook.  While not statistically significant, the control group exhibited better recall, although the visualization group had higher engagement.  That said, the control group exhibited higher course grades, so the difference in learning may actually be from unrelated factors.

Conference Attendance: SIGCSE 2019 - Day 1.5

Back at SIGCSE again, this one the 50th to be held.  Much of my time is spent dashing about and renewing friendships.  That said, I made it to several sessions.  I've included at least one author and linked to their paper.

Starting on day 2, we begin with the Keynote from Mark Guzdial

"The study of computers and all the phenomena associated with them." (Perlis, Newell, and Simon, 1967).  The early uses of Computer Science were proposing its inclusion in education to support all of education (1960s).  For example, given the equation "x = x0 + v*t + 1/2 a * t^2", we can also teach it as a algorithm / program.  The program then shows the causal relation of the components.  Benefiting the learning of other fields by integrating computer science.

Do we have computing for all?  Most high school students have no access, nor do they even take the classes when they do.

Computing is a 21st century literacy.  What is the core literacy that everyone needs?  C.f. K-8 Learning Trajectories Derived from Research Literature: Sequence, Repetition, Conditionals.  Our goal is not teaching Computer Science, but rather supporting learning.

For example, let's learn about acoustics.  Mark explains the straight physics.  Then he brings up a program (in a block-based language) that can display the sound reaching the microphone.  So the learning came from the program, demonstration, and prediction.  Not from writing and understanding the code itself.  Taking data and helping build narratives.

We need to build more, try more, and innovate.  To meet our mission, "to provide a global forum for educators to discuss research and practice related to the learning and teaching of computing at all levels."

Now for the papers from day 1:

Lisa Yan - The PyramidSnapshot Challenge

The core problem is that we only view student work by the completed snapshots.  Extended Eclipse with a plugin to record every compilation, giving 130,000 snapshots from 2600 students.  Into those snapshots, they needed to develop an automated approach to classifying the intermediate snapshots.  Tried autograders and abstract syntax trees, but those could not capture the full space.  But!  The output is an image, so why not try using image classification.  Of the 138531 snapshots, they generated 27220 images.  Lisa then manually labeled 12000 of those images, into 16 labels that are effectively four milestones in development.  Then, a neural network classifier classified the images.  Plot the milestones using a spectrum of colors (blue being start, red being perfect).  Good students quickly reach the complete milestones.  Struggling students are often in early debugging stages.  Tinkering students (~73 percentile on exams) take a lot of time, but mostly spend it on later milestones.  From these, we can review assignments and whether students are in the declared milestones, or if other assignment structure is required.

For the following three papers, I served as the session chair.

Tyler Greer - On the Effects of Active Learning Environments in Computing Education

Replication study on the impact of using an active learning classroom versus traditional room.  Using the same instructor to teach the same course, but using different classrooms and lecture styles (traditional versus peer instruction).  The most significant factor was the use of active learning versus traditional, with no clear impact from the type of room used.

Yayjin Ham, Brandon Myers - Supporting Guided Inquiry with Cooperative Learning in Computer Organization

Taking a computer organization course with peer instruction and guided inquiry, can the peer instruction be traded for cooperative learning to emphasize further engagement and learning.  Exploration of a model (program, documentation), then concept invention (building an understanding), then application (apply the learned concepts to a new problem).  Reflect on the learning at the end of each "lecture".  In back-to-back semesters, measure the learning gains from this intervention, as well as survey on other secondary items (such as, engagement and peer support).  However, the students in the intervention group did worse, most of which is controlled by the prior GPA.  And across the other survey points, students in the intervention group rated lower.  The materials used are available online.

Aman, et al - POGIL in Computer Science: Faculty Motivation and Challenges

Faculty try implementing POGIL in the classroom.  Start with training, then implementing in the classroom, and continued innovation.  Faculty want to see more motivation, retaining the material, and staying in the course (as well as in the program).  Students have a mismatch between their learning and their perceived learning.  There are many challenges and concerns from faculty about the costs of adoption.